Disputed term/author/ism  Author 
Entry 
Reference 

Analogies  Cartwright  I 94 Analogy/Duhem/Cartwright: It is a raw fact that some things sometimes behave like certain other things  that gives us indications. Explanation/Duhem: provides a scheme for these indications. Unification: is fictitious  it is intended to simplify the theory. E.g. Maxwell treated light and electricity as the same. I 111 Analogy/RussellVsAnalogy: the principle "same cause, same effect" is futile  if the antecedent (the circumstances represents) is accurate enough, the same case will never happen again > per >fundamental laws. >Effects, >Causes, >Causality, >Description levels, cf. >Singular Terms. 
Car I N. Cartwright How the laws of physics lie Oxford New York 1983 CartwrightR I R. Cartwright A Neglected Theory of Truth. Philosophical Essays, Cambridge/MA pp. 7193 In Theories of Truth, Paul Horwich Aldershot 1994 CartwrightR II R. Cartwright Ontology and the theory of meaning Chicago 1954 
Bayesian Networks  Norvig  Norvig I 510 Bayesian Networks/belief networks/probabilistic networks/knowledge map/AI research/Norvig/Russell: Bayesian networks can represent essentially any full joint probability distribution and in many cases can do so very concisely. Norvig I 511 A Bayesian network is a directed graph in which each node is annotated with quantitative probability information. The full specification is as follows: 1. Each node corresponds to a random variable, which may be discrete or continuous. 2. A set of directed links or arrows connects pairs of nodes. If there is an arrow from node X to node Y,X is said to be a parent of Y. The graph has no directed cycles (and hence is a directed acyclic graph, or DAG. 3. Each node Xi has a conditional probability distribution P(Xi Parents(Xi)) that quantifies the effect of the parents on the node. The topology of the network  the set of nodes and links  specifies the conditional independence relationships that hold in the domain (…). >Probability theory/Norvig, >Uncertainty/AI research. The intuitive meaning of an arrow is typically that X has a direct influence on Y, which suggests that causes should be parents of effects. Once the topology of the Bayesian network is laid out, we need only specify a conditional probability distribution for each variable, given its parents. Norvig I 512 Circumstances: The probabilities actually summarize a potentially Norvig I 513 Infinite set of circumstances. Norvig I 515 Inconsistency: If there is no redundancy, then there is no chance for inconsistency: it is impossible for the knowledge engineer or domain expert to create a Bayesian network that violates the axioms of probability. Norvig I 517 Diagnostic models: If we try to build a diagnostic model with links from symptoms to causes (…) we end up having to specify additional dependencies between otherwise independent causes (and often between separately occurring symptoms as well). Causal models: If we stick to a causal model, we end up having to specify fewer numbers, and the numbers will often be easier to come up with. In the domain of medicine, for example, it has been shown by Tversky and Kahneman (1982)^{(1)} that expert physicians prefer to give probability judgments for causal rules rather than for diagnostic ones. Norvig I 529 Inference: because it includes inference in propositional logic as a special case, inference in Bayesian networks is NPhard. >NPProblems/Norvig. There is a close connection between the complexity of Bayesian network inference and the complexity of constraint satisfaction problems (CSPs). > Constraint satisfaction problems/Norvig. Clustering algirithms: Using clustering algorithms (also known as join tree algorithms), the time can be reduced to O(n). For this reason, these algorithms are widely used in commercial Bayesian network tools. The basic idea of clustering is to join individual nodes of the network to form cluster nodes in such a way that the resulting network is a polytree. Norvig I 539 (…) Bayesian networks are essentially propositional: the set of random variables is fixed and finite, and each has a fixed domain of possible values. This fact limits the applicability of Bayesian networks. If we can find a way to combine probability theory with the expressive power of firstorder representations, we expect to be able to increase dramatically the range of problems that can be handled. Norvig I 540 Possible worlds/probabilities: for Bayesian networks, the possible worlds are assignments of values to variables; for the Boolean case in particular, the possible worlds are identical to those of propositional logic. For a firstorder probability model, then, it seems we need the possible worlds to be those of firstorder logic—that is, a set of objects with relations among them and an interpretation that maps constant symbols to objects, predicate symbols to relations, and function symbols to functions on those objects. Problem: the set of firstorder models is infinite. Solution: The database semantics makes the unique names assumption—here, we adopt it for the constant symbols. It also assumes domain closure  there are no more objects than those that are named. We can then guarantee a finite set of possible worlds by making the set of objects in each world be exactly the set of constant Norvig I 541 Symbols that are used. There is no uncertainty about the mapping from symbols to objects or about the objects that exist. Relational probability models: We will call models defined in this way relational probability models, or RPMs. The name relational probability model was given by Pfeffer (2000)^{(2)} to a slightly different representation, but the underlying ideas are the same. >Uncertainty/AI research. Norvig I 552 Judea Pearl developed the messagepassing method for carrying out inference in tree networks (Pearl, 1982a)^{(3)} and polytree networks (Kim and Pearl, 1983)^{(4)} and explained the importance of causal rather than diagnostic probability models, in contrast to the certaintyfactor systems then in vogue. The first expert system using Bayesian networks was CONVINCE (Kim, 1983)^{(5)}. Early applications in medicine included the MUNIN system for diagnosing neuromuscular disorders (Andersen et al., 1989)^{(6)} and the PATHFINDER system for pathology (Heckerman, 1991)^{(7)}. Norvig I 553 Perhaps the most widely used Bayesian network systems have been the diagnosis and repair modules (e.g., the PrinterWizard) in Microsoft Windows (Breese and Heckerman, 1996)^{(8)} and the Office Assistant in Microsoft Office (Horvitz et al., 1998)^{(9)}. Another important application area is biology: Bayesian networks have been used for identifying human genes by reference to mouse genes (Zhang et al., 2003)^{(10)}, inferring cellular networks Friedman (2004)^{(11)}, and many other tasks in bioinformatics. We could go on, but instead we’ll refer you to Pourret et al. (2008)^{(12)}, a 400page guide to applications of Bayesian networks. Ross Shachter (1986)^{(13)}, working in the influence diagram community, developed the first complete algorithm for general Bayesian networks. His method was based on goaldirected reduction of the network using posteriorpreserving transformations. Pearl (1986)^{(14)} developed a clustering algorithm for exact inference in general Bayesian networks, utilizing a conversion to a directed polytree of clusters in which message passing was used to achieve consistency over variables shared between clusters. A similar approach, developed by the statisticians David Spiegelhalter and Steffen Lauritzen (Lauritzen and Spiegelhalter, 1988)^{(15)}, is based on conversion to an undirected form of graphical model called a Markov network. This approach is implemented in the HUGIN system, an efficient and widely used tool for uncertain reasoning (Andersen et al., 1989)^{(6)}. Boutilier et al. (1996)^{(16)} show how to exploit contextspecific independence in clustering algorithms. Norvig I 604 Dynamic Bayesian networks (DBNs): can be viewed as a sparse encoding of a Markov process and were first used in AI by Dean and Kanazawa (1989b)^{(17)}, Nicholson and Brady (1992)^{(18)}, and Kjaerulff (1992)^{(19)}. The last work extends the HUGIN Bayes net system to accommodate dynamic Bayesian networks. The book by Dean and Wellman (1991)^{(20)} helped popularize DBNs and the probabilistic approach to planning and control within AI. Murphy (2002)^{(21)} provides a thorough analysis of DBNs. Dynamic Bayesian networks have become popular for modeling a variety of complex motion processes in computer vision (Huang et al., 1994^{(22)}; Intille and Bobick, 1999)^{(23)}. Like HMMs, they have found applications in speech recognition (Zweig and Russell, 1998^{(24)}; Richardson et al., 2000^{(25)}; Stephenson et al., 2000^{(26)}; Nefian et al., 2002^{(27)}; Livescu et al., 2003^{(28)}), Norvig I 605 genomics (Murphy and Mian, 1999^{(29)}; Perrin et al., 2003^{(30)}; Husmeier, 2003^{(31)}) and robot localization (Theocharous et al., 2004)^{(32)}. The link between HMMs and DBNs, and between the forward–backward algorithm and Bayesian network propagation, was made explicitly by Smyth et al. (1997)^{(33)}. A further unification with Kalman filters (and other statistical models) appears in Roweis and Ghahramani (1999)^{(34)}. Procedures exist for learning the parameters (Binder et al., 1997a^{(35)}; Ghahramani, 1998^{(36)}) and structures (Friedman et al., 1998)^{(37)} of DBNs. 1. Tversky, A. and Kahneman, D. (1982). Causal schemata in judgements under uncertainty. In Kahneman, D., Slovic, P., and Tversky, A. (Eds.), Judgement Under Uncertainty: Heuristics and Biases. Cambridge University Press. 2. Pfeffer, A. (2000). Probabilistic Reasoning for Complex Systems. Ph.D. thesis, Stanford University 3. Pearl, J. (1982a). Reverend Bayes on inference engines: A distributed hierarchical approach. In AAAI 82, pp. 133–136 4. Kim, J. H. and Pearl, J. (1983). A computational model for combined causal and diagnostic reasoning in inference systems. In IJCAI83, pp. 190–193. 5. Kim, J. H. (1983). CONVINCE: A Conversational Inference Consolidation Engine. Ph.D. thesis, Department of Computer Science, University of California at Los Angeles. 6. Andersen, S. K., Olesen, K. G., Jensen, F. V., and Jensen, F. (1989). HUGIN—A shell for building Bayesian belief universes for expert systems. In IJCAI89, Vol. 2, pp. 1080–1085. 7. Heckerman, D. (1991). Probabilistic Similarity Networks. MIT Press. 8. Breese, J. S. and Heckerman, D. (1996). Decisiontheoretic troubleshooting: A framework for repair and experiment. In UAI96, pp. 124–132. 9. Horvitz, E. J., Breese, J. S., Heckerman, D., and Hovel, D. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In UAI98, pp. 256–265. 10. Zhang, L., Pavlovic, V., Cantor, C. R., and Kasif, S. (2003). Humanmouse gene identification by comparative evidence integration and evolutionary analysis. Genome Research, pp. 1–13. 11. Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303(5659), 799–805. 12. Pourret, O., Naım, P., and Marcot, B. (2008). Bayesian Networks: A practical guide to applications. Wiley. 13. Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34, 871–882. 14. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. AIJ, 29, 241–288. 15. Lauritzen, S. and Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. J. Royal Statistical Society, B 50(2), 157–224. 16. Boutilier, C., Friedman, N., Goldszmidt, M., and Koller, D. (1996). Contextspecific independence in Bayesian networks. In UAI96, pp. 115–123. 17. Dean, T. and Kanazawa, K. (1989b). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. 18. Nicholson, A. and Brady, J. M. (1992). The data association problem when monitoring robot vehicles using dynamic belief networks. In ECAI92, pp. 689–693. 19. Kjaerulff, U. (1992). A computational scheme for reasoning in dynamic probabilistic networks. In UAI92, pp. 121–129. 20. Dean, T. and Wellman, M. P. (1991). Planning and Control. Morgan Kaufmann. 21. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley 22. Huang, T., Koller, D., Malik, J., Ogasawara, G., Rao, B., Russell, S. J., and Weber, J. (1994). Automatic symbolic traffic scene analysis using belief networks. In AAAI94, pp. 966–972 23. Intille, S. and Bobick, A. (1999). A framework for recognizing multiagent action from visual evidence. In AAAI99, pp. 518–525. 24. Zweig, G. and Russell, S. J. (1998). Speech recognition with dynamic Bayesian networks. In AAAI98, pp. 173–180. 25. Richardson, M., Bilmes, J., and Diorio, C. (2000). Hiddenarticulator Markov models: Performance improvements and robustness to noise. In ICASSP00. 26. Stephenson, T., Bourlard, H., Bengio, S., and Morris, A. (2000). Automatic speech recognition using dynamic bayesian networks with both acoustic and articulatory features. In ICSLP00, pp. 951954. 27. Nefian, A., Liang, L., Pi, X., Liu, X., and Murphy, K. (2002). Dynamic bayesian networks for audiovisual speech recognition. EURASIP, Journal of Applied Signal Processing, 11, 1–15. 28. Livescu, K., Glass, J., and Bilmes, J. (2003). Hidden feature modeling for speech recognition using dynamic Bayesian networks. In EUROSPEECH2003, pp. 2529–2532 29. Murphy, K. and Mian, I. S. (1999). Modelling gene expression data using Bayesian networks. people.cs.ubc.ca/˜murphyk/Papers/ismb99.pdf. 30. Perrin, B. E., Ralaivola, L., and Mazurie, A. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics, 19, II 138II 148. 31. Husmeier, D. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics, 19(17), 22712282. 32. Theocharous, G., Murphy, K., and Kaelbling, L. P. (2004). Representing hierarchical POMDPs as DBNs for multiscale robot localization. In ICRA04. 33. Smyth, P., Heckerman, D., and Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9(2), 227–269. 34. Roweis, S. T. and Ghahramani, Z. (1999). A unifying review of Linear GaussianModels. Neural Computation, 11(2), 305–345. 35. Binder, J., Koller, D., Russell, S. J., and Kanazawa, K. (1997a). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213–244. 36. Ghahramani, Z. (1998). Learning dynamic bayesian networks. In Adaptive Processing of Sequences and Data Structures, pp. 168–197. 37. Friedman, N., Murphy, K., and Russell, S. J. (1998). Learning the structure of dynamic probabilistic networks. In UAI98. 
Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 
Bayesian Networks  Russell  Norvig I 510 Bayesian Networks/belief networks/probabilistic networks/knowledge map/AI research/Norvig/Russell: Bayesian networks can represent essentially any full joint probability distribution and in many cases can do so very concisely. Norvig I 511 A Bayesian network is a directed graph in which each node is annotated with quantitative probability information. The full specification is as follows: 1. Each node corresponds to a random variable, which may be discrete or continuous. 2. A set of directed links or arrows connects pairs of nodes. If there is an arrow from node X to node Y,X is said to be a parent of Y. The graph has no directed cycles (and hence is a directed acyclic graph, or DAG. 3. Each node Xi has a conditional probability distribution P(Xi Parents(Xi)) that quantifies the effect of the parents on the node. The topology of the network  the set of nodes and links  specifies the conditional independence relationships that hold in the domain (…). >Probability theory/Norvig, >Uncertainty/AI research. The intuitive meaning of an arrow is typically that X has a direct influence on Y, which suggests that causes should be parents of effects. Once the topology of the Bayesian network is laid out, we need only specify a conditional probability distribution for each variable, given its parents. Norvig I 512 Circumstances: The probabilities actually summarize a potentially Norvig I 513 Infinite set of circumstances. Norvig I 515 Inconsistency: If there is no redundancy, then there is no chance for inconsistency: it is impossible for the knowledge engineer or domain expert to create a Bayesian network that violates the axioms of probability. Norvig I 517 Diagnostic models: If we try to build a diagnostic model with links from symptoms to causes (…) we end up having to specify additional dependencies between otherwise independent causes (and often between separately occurring symptoms as well). Causal models: If we stick to a causal model, we end up having to specify fewer numbers, and the numbers will often be easier to come up with. In the domain of medicine, for example, it has been shown by Tversky and Kahneman (1982)^{(1)} that expert physicians prefer to give probability judgments for causal rules rather than for diagnostic ones. Norvig I 529 Inference: because it includes inference in propositional logic as a special case, inference in Bayesian networks is NPhard. >NPProblems/Norvig. There is a close connection between the complexity of Bayesian network inference and the complexity of constraint satisfaction problems (CSPs). >Constraint satisfaction problems/Norvig. Clustering algirithms: Using clustering algorithms (also known as join tree algorithms), the time can be reduced to O(n). For this reason, these algorithms are widely used in commercial Bayesian network tools. The basic idea of clustering is to join individual nodes of the network to form cluster nodes in such a way that the resulting network is a polytree. Norvig I 539 (…) Bayesian networks are essentially propositional: the set of random variables is fixed and finite, and each has a fixed domain of possible values. This fact limits the applicability of Bayesian networks. If we can find a way to combine probability theory with the expressive power of firstorder representations, we expect to be able to increase dramatically the range of problems that can be handled. Norvig I 540 Possible worlds/probabilities: for Bayesian networks, the possible worlds are assignments of values to variables; for the Boolean case in particular, the possible worlds are identical to those of propositional logic. For a firstorder probability model, then, it seems we need the possible worlds to be those of firstorder logic—that is, a set of objects with relations among them and an interpretation that maps constant symbols to objects, predicate symbols to relations, and function symbols to functions on those objects. Problem: the set of firstorder models is infinite. Solution: The database semantics makes the unique names assumption—here, we adopt it for the constant symbols. It also assumes domain closure  there are no more objects than those that are named. We can then guarantee a finite set of possible worlds by making the set of objects in each world be exactly the set of constant Norvig I 541 Symbols that are used. There is no uncertainty about the mapping from symbols to objects or about the objects that exist. Relational probability models: We will call models defined in this way relational probability models, or RPMs. The name relational probability model was given by Pfeffer (2000)^{(2)} to a slightly different representation, but the underlying ideas are the same. >Uncertainty/AI research. Norvig I 552 Judea Pearl developed the messagepassing method for carrying out inference in tree networks (Pearl, 1982a)^{(3)} and polytree networks (Kim and Pearl, 1983)^{(4)} and explained the importance of causal rather than diagnostic probability models, in contrast to the certaintyfactor systems then in vogue. The first expert system using Bayesian networks was CONVINCE (Kim, 1983)^{(5)}. Early applications in medicine included the MUNIN system for diagnosing neuromuscular disorders (Andersen et al., 1989)^{(6)} and the PATHFINDER system for pathology (Heckerman, 1991)^{(7)}. Norvig I 553 Perhaps the most widely used Bayesian network systems have been the diagnosis and repair modules (e.g., the PrinterWizard) in Microsoft Windows (Breese and Heckerman, 1996)^{(8)} and the Office Assistant in Microsoft Office (Horvitz et al., 1998)^{(9)}. Another important application area is biology: Bayesian networks have been used for identifying human genes by reference to mouse genes (Zhang et al., 2003)^{(10)}, inferring cellular networks Friedman (2004)^{(11)}, and many other tasks in bioinformatics. We could go on, but instead we’ll refer you to Pourret et al. (2008)^{(12)}, a 400page guide to applications of Bayesian networks. Ross Shachter (1986)^{(13)}, working in the influence diagram community, developed the first complete algorithm for general Bayesian networks. His method was based on goaldirected reduction of the network using posteriorpreserving transformations. Pearl (1986)^{(14)} developed a clustering algorithm for exact inference in general Bayesian networks, utilizing a conversion to a directed polytree of clusters in which message passing was used to achieve consistency over variables shared between clusters. A similar approach, developed by the statisticians David Spiegelhalter and Steffen Lauritzen (Lauritzen and Spiegelhalter, 1988)^{(15)}, is based on conversion to an undirected form of graphical model called a Markov network. This approach is implemented in the HUGIN system, an efficient and widely used tool for uncertain reasoning (Andersen et al., 1989)^{(6)}. Boutilier et al. (1996)^{(16)} show how to exploit contextspecific independence in clustering algorithms. Norvig I 604 Dynamic Bayesian networks (DBNs): can be viewed as a sparse encoding of a Markov process and were first used in AI by Dean and Kanazawa (1989b)^{(17)}, Nicholson and Brady (1992)^{(18)}, and Kjaerulff (1992)^{(19)}. The last work extends the HUGIN Bayes net system to accommodate dynamic Bayesian networks. The book by Dean and Wellman (1991)^{(20)} helped popularize DBNs and the probabilistic approach to planning and control within AI. Murphy (2002)^{(21)} provides a thorough analysis of DBNs. Dynamic Bayesian networks have become popular for modeling a variety of complex motion processes in computer vision (Huang et al., 1994^{(22)}; Intille and Bobick, 1999)^{(23)}. Like HMMs, they have found applications in speech recognition (Zweig and Russell, 1998^{(24)}; Richardson et al., 2000^{(25)}; Stephenson et al., 2000^{(26)}; Nefian et al., 2002^{(27)}; Livescu et al., 2003^{(28)}), Norvig I 605 genomics (Murphy and Mian, 1999^{(29)}; Perrin et al., 2003^{(30)}; Husmeier, 2003^{(31)}) and robot localization (Theocharous et al., 2004)^{(32)}. The link between HMMs and DBNs, and between the forward–backward algorithm and Bayesian network propagation, was made explicitly by Smyth et al. (1997)^{(33)}. A further unification with Kalman filters (and other statistical models) appears in Roweis and Ghahramani (1999)^{(34)}. Procedures exist for learning the parameters (Binder et al., 1997a^{(35)}; Ghahramani, 1998^{(36)}) and structures (Friedman et al., 1998)^{(37)} of DBNs. 1. Tversky, A. and Kahneman, D. (1982). Causal schemata in judgements under uncertainty. In Kahneman, D., Slovic, P., and Tversky, A. (Eds.), Judgement Under Uncertainty: Heuristics and Biases. Cambridge University Press. 2. Pfeffer, A. (2000). Probabilistic Reasoning for Complex Systems. Ph.D. thesis, Stanford University 3. Pearl, J. (1982a). Reverend Bayes on inference engines: A distributed hierarchical approach. In AAAI 82, pp. 133–136 4. Kim, J. H. and Pearl, J. (1983). A computational model for combined causal and diagnostic reasoning in inference systems. In IJCAI83, pp. 190–193. 5. Kim, J. H. (1983). CONVINCE: A Conversational Inference Consolidation Engine. Ph.D. thesis, Department of Computer Science, University of California at Los Angeles. 6. Andersen, S. K., Olesen, K. G., Jensen, F. V., and Jensen, F. (1989). HUGIN—A shell for building Bayesian belief universes for expert systems. In IJCAI89, Vol. 2, pp. 1080–1085. 7. Heckerman, D. (1991). Probabilistic Similarity Networks. MIT Press. 8. Breese, J. S. and Heckerman, D. (1996). Decisiontheoretic troubleshooting: A framework for repair and experiment. In UAI96, pp. 124–132. 9. Horvitz, E. J., Breese, J. S., Heckerman, D., and Hovel, D. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In UAI98, pp. 256–265. 10. Zhang, L., Pavlovic, V., Cantor, C. R., and Kasif, S. (2003). Humanmouse gene identification by comparative evidence integration and evolutionary analysis. Genome Research, pp. 1–13. 11. Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303(5659), 799–805. 12. Pourret, O., Naım, P., and Marcot, B. (2008). Bayesian Networks: A practical guide to applications. Wiley. 13. Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34, 871–882. 14. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. AIJ, 29, 241–288. 15. Lauritzen, S. and Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. J. Royal Statistical Society, B 50(2), 157–224. 16. Boutilier, C., Friedman, N., Goldszmidt, M., and Koller, D. (1996). Contextspecific independence in Bayesian networks. In UAI96, pp. 115–123. 17. Dean, T. and Kanazawa, K. (1989b). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. 18. Nicholson, A. and Brady, J. M. (1992). The data association problem when monitoring robot vehicles using dynamic belief networks. In ECAI92, pp. 689–693. 19. Kjaerulff, U. (1992). A computational scheme for reasoning in dynamic probabilistic networks. In UAI92, pp. 121–129. 20. Dean, T. and Wellman, M. P. (1991). Planning and Control. Morgan Kaufmann. 21. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley 22. Huang, T., Koller, D., Malik, J., Ogasawara, G., Rao, B., Russell, S. J., and Weber, J. (1994). Automatic symbolic traffic scene analysis using belief networks. In AAAI94, pp. 966–972 23. Intille, S. and Bobick, A. (1999). A framework for recognizing multiagent action from visual evidence. In AAAI99, pp. 518–525. 24. Zweig, G. and Russell, S. J. (1998). Speech recognition with dynamic Bayesian networks. In AAAI98, pp. 173–180. 25. Richardson, M., Bilmes, J., and Diorio, C. (2000). Hiddenarticulator Markov models: Performance improvements and robustness to noise. In ICASSP00. 26. Stephenson, T., Bourlard, H., Bengio, S., and Morris, A. (2000). Automatic speech recognition using dynamic bayesian networks with both acoustic and articulatory features. In ICSLP00, pp. 951954. 27. Nefian, A., Liang, L., Pi, X., Liu, X., and Murphy, K. (2002). Dynamic bayesian networks for audiovisual speech recognition. EURASIP, Journal of Applied Signal Processing, 11, 1–15. 28. Livescu, K., Glass, J., and Bilmes, J. (2003). Hidden feature modeling for speech recognition using dynamic Bayesian networks. In EUROSPEECH2003, pp. 2529–2532 29. Murphy, K. and Mian, I. S. (1999). Modelling gene expression data using Bayesian networks. people.cs.ubc.ca/˜murphyk/Papers/ismb99.pdf. 30. Perrin, B. E., Ralaivola, L., and Mazurie, A. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics, 19, II 138II 148. 31. Husmeier, D. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics, 19(17), 22712282. 32. Theocharous, G., Murphy, K., and Kaelbling, L. P. (2004). Representing hierarchical POMDPs as DBNs for multiscale robot localization. In ICRA04. 33. Smyth, P., Heckerman, D., and Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9(2), 227–269. 34. Roweis, S. T. and Ghahramani, Z. (1999). A unifying review of Linear GaussianModels. Neural Computation, 11(2), 305–345. 35. Binder, J., Koller, D., Russell, S. J., and Kanazawa, K. (1997a). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213–244. 36. Ghahramani, Z. (1998). Learning dynamic bayesian networks. In Adaptive Processing of Sequences and Data Structures, pp. 168–197. 37. Friedman, N., Murphy, K., and Russell, S. J. (1998). Learning the structure of dynamic probabilistic networks. In UAI98. 
Russell I B. Russell/A.N. Whitehead Principia Mathematica Frankfurt 1986 Russell II B. Russell The ABC of Relativity, London 1958, 1969 German Edition: Das ABC der Relativitätstheorie Frankfurt 1989 Russell IV B. Russell The Problems of Philosophy, Oxford 1912 German Edition: Probleme der Philosophie Frankfurt 1967 Russell VI B. Russell "The Philosophy of Logical Atomism", in: B. Russell, Logic and KNowledge, ed. R. Ch. Marsh, London 1956, pp. 200202 German Edition: Die Philosophie des logischen Atomismus In Eigennamen, U. Wolf (Hg) Frankfurt 1993 Russell VII B. Russell On the Nature of Truth and Falsehood, in: B. Russell, The Problems of Philosophy, Oxford 1912  Dt. "Wahrheit und Falschheit" In Wahrheitstheorien, G. Skirbekk (Hg) Frankfurt 1996 Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 
Behavior  Lorenz  Page numbers here from the German edition: K. Lorenz, Das sogenannte Böse Wien, 1963 II 34 Aggression/Lorenz: two types: A) between different species B) within one species. (Subject of this book). II 36 "Mobbing"/"hate on"/Lorenz: crows, and other birds "hate on" the owl they have discovered by day: (s) They show their fellowspecies by gestures where the enemy is. For example a jay follows the fox screeching through the forest. This is a passing on of a noninnate knowledge. II 47 Singing/Birds/Lorenz: Birds share, among others things, also the age. II 58 Aggression/Lorenz: Thesis: More than other properties, the aggressive behavior can be exaggerated by its pernicious effect in the grotesque and unsuitable. For us, it is the inheritance of the intraspecific selection, which has lasted over decades. The evil introspecific selection must be taken. II 72 Stimulus/Reaction/Behavior/Lorenz: Experiments show that (in captivity) the withdrawal of stimuli decreases the threshold for triggering reactions. At the end, a room corner is performed courtship to because it is the only visual point of view. II 81 Aggression/Evolution/Lorenz: The reorientation of the attack is probably the most ingenious source that was created by the species change, in order to divert aggression into harmless pathways. II 91 Behavior/ritualization/Lorenz: e.g. in the insect world, it may be the case that behavior is even be embodied. E.g. robbery or assassination: the suitor hands over a prey to the beloved of the right size so that he can have sexual intercourse with the female during her eating the prey without being eaten himself. II 92 Later generations react only to a corresponding symbol. Congenital understanding. II 93 Behavior/animal/ritualization/Lorenz: it would be a mistake to call ritualized "rushing" an "expression" of love "or the affiliation of the female to the spouse. The independent instinctual movement is not a byproduct, not an epiphenomenon of the tie that holds the animals together, but it is itself this tie. A completely autonomous, new instinct. II 103 Rite/behavior/animal/Lorenz: the most important function is the active drive to social behavior. II 104 New function: communication. II 105 The unification of the variable variety of possibilities of action into a single rigid sequence reduces the danger of ambiguity in the communication. II 108 "Good manners" are those that characterize their own group. II 109 Any deviation causes aggression, so the group is forced to act in a unified way. >Aggression. II 123 Behavioral research/Lorenz: in the heroic time of comparative behavioral research, it was thought that only one instinct dominated this, but exclusively, one animal. J. HuxleyVs: Human and animal are like a ship commanded by many captains. Animals have this agreement that only one of them can enter the command bridge.  Gould I 105 Lorenz's thesis: we do not react to wholenesses or shapes, but to a group of special mechanisms that act as triggers. (Lorenz, 1950 "Ganzheit und Teil") It is well known that birds, in particular, react to abstract features and not to shapes.  Gould VIII 34 Aggression/Lorenz: (The socalled Evil): Thesis: Aggression is a speciespreserving function. Thus only the most suitable individuals are propagated. DawkinsVsLorenz: Prime example for a circular reasoning. It is also contrary to Darwinism, which he does not seem to have noticed. 
Lorenz I K. Lorenz Das sogenannte Böse Wien 1963 Gould I Stephen Jay Gould The Panda’s Thumb. More Reflections in Natural History, New York 1980 German Edition: Der Daumen des Panda Frankfurt 2009 Gould II Stephen Jay Gould Hen’s Teeth and Horse’s Toes. Further Reflections in Natural History, New York 1983 German Edition: Wie das Zebra zu seinen Streifen kommt Frankfurt 1991 Gould III Stephen Jay Gould Full House. The Spread of Excellence from Plato to Darwin, New York 1996 German Edition: Illusion Fortschritt Frankfurt 2004 Gould IV Stephen Jay Gould The Flamingo’s Smile. Reflections in Natural History, New York 1985 German Edition: Das Lächeln des Flamingos Basel 1989 
Causality  James  DiazBone I 130 World/James: instead of the question of unity and multiplicity, the view is of particular importance that it is a spacetime continuum. I 131 Unity and diversity are absolutely equivalent here! I 131 Causality/James: can then be spoken of causal unit or purposeunit of the world at all? Multiplicity can be regarded as eternal as causal unification. >Unity, >Unity and Multiplicity. 
James I R. DiazBone/K. Schubert William James zur Einführung Hamburg 1996 
Change  AI Research  Norvig I 566 Change/probability/time/inference/AI research/Norvig/Russell: Agents in partially observable environments must be able to keep track of the current state, to the extent that their sensors allow. (…) an agent maintains a belief state that represents which states of the world are currently possible. >Belief states/Norvig. From the belief state and a transition model, the agent can predict how the world might evolve in the next time step. From the percepts observed and a sensor model, the agent can update the belief state. [There are two ways of representing belief states] (…) a) by explicitly enumerated sets of states, b) by logical formulas. Those approaches defined belief states in terms of which world states were possible, but could say nothing about which states were likely or unlikely. Problem: a changing world is modeled using a variable for each aspect of the world state at each point in time. The transition and sensor models may be uncertain: the transition model describes the probability distribution of the variables at time t, given the state of the world at past times, while the sensor model describes the probability of each percept at time t, given the current state of the world. Solution: three specific kinds of models: hidden Markov models, Kalman filters, and dynamic Bayesian networks (which include hidden Markov models and Kalman filters as special cases). Norvig I 567 To assess the current state from the history of evidence and to predict the outcomes of treatment actions, we must model these changes. We view the world as a series of snapshots, or time slices, each of which contains a set of random variables, some observable and some not. ((s) Cf. >Four dimensionalism/Philosophical theories). Norvig I 568 (…) the next step is to specify how the world evolves (the transition model) and how the evidence variables get their values (the sensor model). Norvig I 570 Order: increasing the order can always be reformulated as an increase in the set of state variables, keeping the order fixed. Notice that adding state variables might improve the system’s predictive power but also increases the prediction requirements (…). Norvig I 603 Problem: data association: When trying to keep track of many objects, uncertainty arises as to which observations belong to which objects—the data association problem. The number of association hypotheses is typically intractably large, but MCMC and particle filtering algorithms for data association work well in practice. Norvig I 602 MCMC: An MCMC algorithm explores the space of assignment histories. Norvig I 603 Change: The changing state of the world is handled by using a set of random variables to represent the state at each point in time. Representations: can be designed to satisfy the Markov property, so that the future is independent of the past given the present. Combined with the assumption that the process is stationary—that is, the dynamics do not change over time—this greatly simplifies the representation. Probability: A temporal probability model can be thought of as containing a transition model describing the state evolution and a sensor model describing the observation process. >Inference/AI research. Historical development: Many of the basic ideas for estimating the state of dynamical systems came from the mathematician C. F. Gauss (1809)^{(1)}, who formulated a deterministic leastsquares algorithm for the problem of estimating orbits from astronomical observations. A. A. Markov (1913)^{(2)} developed what was later called the Markov assumption in his analysis of stochastic processes; Norvig I 604 (…). The general theory of Markov chains and their mixing times is covered by Levin et al. (2008)^{(3)}. Significant classified work on filtering was done during World War II by Wiener (1942)^{(4)} for continuoustime processes and by Kolmogorov (1941)^{(5)} for discretetime processes. Although this work led to important technological developments over the next 20 years, its use of a frequencydomain representation made many calculations quite cumbersome. Direct statespace modeling of the stochastic process turned out to be simpler, as shown by Peter Swerling (1959)^{(6)} and Rudolf Kalman (1960)^{(7)}. The hidden Markov model and associated algorithms for inference and learning, including the forward–backward algorithm, were developed by Baum and Petrie (1966)^{(8)}. The Viterbi algorithm first appeared in (Viterbi, 1967)^{(9)}. Similar ideas also appeared independently in the Kalman filtering community (Rauch et al., 1965)^{(10)}. The forward–backward algorithm was one of the main precursors of the general formulation of the EM algorithm (Dempster et al., 1977)^{(11)} (…). Dynamic Bayesian networks (DBNs) can be viewed as a sparse encoding of a Markov process and were first used in AI by Dean and Kanazawa (1989b)^{(12)}, Nicholson and Brady (1992)^{(13)}, and Kjaerulff (1992)^{(14)}. The last work extends the HUGIN Bayes net system to accommodate dynamic Bayesian networks. The book by Dean and Wellman (1991)^{(15)} helped popularize DBNs and the probabilistic approach to planning and control within AI. Murphy (2002)^{(16)} provides a thorough analysis of DBNs. Dynamic Bayesian networks have become popular for modeling a variety of complex motion processes in computer vision (Huang et al., 1994^{(17)}; Intille and Bobick, 1999)^{(18)}. Like HMMs, they have found applications in speech recognition (Zweig and Russell, 1998(19)); Richardson et al., 2000^{(20)}; Stephenson et al., 2000^{(21)}; Nefian et al., 2002^{(22)}; Livescu et al., 2003^{(23)}), Norvig I 605 genomics (Murphy and Mian, 1999^{(24)}; Perrin et al., 2003^{(25)}; Husmeier, 2003^{(26)}) and robot localization (Theocharous et al., 2004)^{(27)}. The link between HMMs and DBNs, and between the forward–backward algorithm and Bayesian network propagation, was made explicitly by Smyth et al. (1997)^{(28)}. A further unification with Kalman filters (and other statistical models) appears in Roweis and Ghahramani (1999)^{(29)}. Procedures exist for learning the parameters (Binder et al., 1997a^{(30)}; Ghahramani, 1998)^{(31)} and structures (Friedman et al., 1998)^{(32)} of DBNs. Norvig I 606 Data association: Data association for multi target tracking was first described in a probabilistic setting by Sittler (1964)^{(33)}. The first practical algorithm for largescale problems was the “multiple hypothesis tracker” or MHT algorithm (Reid, 1979)^{(34)}. Many important papers are collected by BarShalom and Fortmann (1988)^{(35)} and BarShalom (1992)^{(36)}. The development of an MCMC algorithm for data association is due to Pasula et al. (1999)^{(37)}, who applied it to traffic surveillance problems. Oh et al. (2009)^{(38)} provide a formal analysis and extensive experimental comparisons to other methods. Schulz et al. (2003)^{(39)} describe a data association method based on particle filtering. Ingemar Cox analyzed the complexity of data association (Cox, 1993^{(40)}; Cox and Hingorani, 1994^{(41)}) and brought the topic to the attention of the vision community. He also noted the applicability of the polynomialtime Hungarian algorithm to the problem of finding mostlikely assignments, which had long been considered an intractable problem in the tracking community. The algorithm itself was published by Kuhn (1955)^{(42)}, based on translations of papers published in 1931 by two Hungarian mathematicians, Dénes König and Jenö Egerváry. The basic theorem had been derived previously, however, in an unpublished Latin manuscript by the famous Prussian mathematician Carl Gustav Jacobi (1804–1851). 1. Gauss, C. F. (1829). Beiträge zur Theorie der algebraischen Gleichungen. Collected in Werke, Vol. 3, pages 71–102. K. Gesellschaft Wissenschaft, Göttingen, Germany, 1876. 2. Markov, A. A. (1913). An example of statistical investigation in the text of “Eugene Onegin” illustrating coupling of “tests” in chains. Proc. Academy of Sciences of St. Petersburg, 7. 3. Levin, D. A., Peres, Y., and Wilmer, E. L. (2008). Markov Chains and Mixing Times. American Mathematical Society. 4. Wiener, N. (1942). The extrapolation, interpolation, and smoothing of stationary time series. Osrd 370, Report to the Services 19, Research Project DIC6037, MIT. 5. Kolmogorov, A. N. (1941). Interpolation und Extrapolation von stationären zufälligen Folgen. Bulletin of the Academy of Sciences of the USSR, Ser. Math. 5, 3–14. 6. Swerling, P. (1959). First order error propagation in a stagewise smoothing procedure for satellite observations. J. Astronautical Sciences, 6, 46–52. 7. Kalman, R. (1960). A new approach to linear filtering and prediction problems. J. Basic Engineering, 82, 35–46. 8. Baum, L. E. and Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics, 41. 9. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269. 10. Rauch, H. E., Tung, F., and Striebel, C. T. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8), 1445–1450. 11. Dempster, A. P., Laird, N., and Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society, 39 (Series B), 1–38. 12. Dean, T. and Kanazawa, K. (1989b). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. 13. Nicholson, A. and Brady, J. M. (1992). The data association problem when monitoring robot vehicles using dynamic belief networks. In ECAI92, pp. 689–693. 14. Kjaerulff, U. (1992). A computational scheme for reasoning in dynamic probabilistic networks. In UAI92, pp. 121–129. 15. Dean, T. and Wellman, M. P. (1991). Planning and Control. Morgan Kaufmann. 16. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley 17. Huang, T., Koller, D., Malik, J., Ogasawara, G., Rao, B., Russell, S. J., and Weber, J. (1994). Automatic symbolic traffic scene analysis using belief networks. In AAAI94, pp. 966–972 18. Intille, S. and Bobick, A. (1999). A framework for recognizing multiagent action from visual evidence. In AAAI99, pp. 518525. 19. Zweig, G. and Russell, S. J. (1998). Speech recognition with dynamic Bayesian networks. In AAAI98, pp. 173–180. 20. Richardson, M., Bilmes, J., and Diorio, C. (2000). Hiddenarticulator Markov models: Performance improvements and robustness to noise. In ICASSP00. 21. Stephenson, T., Bourlard, H., Bengio, S., and Morris, A. (2000). Automatic speech recognition using dynamic bayesian networks with both acoustic and articulatory features. In ICSLP00, pp. 951954. 22. Nefian, A., Liang, L., Pi, X., Liu, X., and Murphy, K. (2002). Dynamic bayesian networks for audiovisual speech recognition. EURASIP, Journal of Applied Signal Processing, 11, 1–15. 23. Livescu, K., Glass, J., and Bilmes, J. (2003). Hidden feature modeling for speech recognition using dynamic Bayesian networks. In EUROSPEECH2003, pp. 25292532 24. Murphy, K. and Mian, I. S. (1999). Modelling gene expression data using Bayesian networks. people.cs.ubc.ca/˜murphyk/Papers/ismb99.pdf. 25. Perrin, B. E., Ralaivola, L., and Mazurie, A. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics, 19, II 138II 148. 26. Husmeier, D. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics, 19(17), 22712282. 27. Theocharous, G., Murphy, K., and Kaelbling, L. P. (2004). Representing hierarchical POMDPs as DBNs for multiscale robot localization. In ICRA04. 28. Smyth, P., Heckerman, D., and Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9(2), 227269. 29. Roweis, S. T. and Ghahramani, Z. (1999). A unifying review of Linear GaussianModels. Neural Computation, 11(2), 305345. 30. Binder, J., Koller, D., Russell, S. J., and Kanazawa, K. (1997a). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213244. 31. Ghahramani, Z. (1998). Learning dynamic bayesian networks. In Adaptive Processing of Sequences and Data Structures, pp. 168–197. 32. Friedman, N., Murphy, K., and Russell, S. J. (1998). Learning the structure of dynamic probabilistic networks. In UAI98. 33. Sittler, R. W. (1964). An optimal data association problem in surveillance theory. IEEE Transactions on Military Electronics, 8(2), 125139. 34. Reid, D. B. (1979). An algorithm for tracking multiple targets. IEEE Trans. Automatic Control, 24(6), 843–854. 35. BarShalom, Y. and Fortmann, T. E. (1988). Tracking and Data Association. Academic Press. 36. BarShalom, Y. (Ed.). (1992). Multi target multi sensor tracking: Advanced applications. Artech House. 37. Pasula, H., Russell, S. J., Ostland, M., and Ritov, Y. (1999). Tracking many objects with many sensors. In IJCAI99. 38. Oh, S., Russell, S. J., and Sastry, S. (2009). Markov chain Monte Carlo data association for multitarget tracking. IEEE Transactions on Automatic Control, 54(3), 481497. 39. Schulz, D., Burgard, W., Fox, D., and Cremers, A. B. (2003). People tracking with mobile robots using samplebased joint probabilistic data association filters. Int. J. Robotics Research, 22(2), 99116 40. Cox, I. (1993). A review of statistical data association techniques for motion correspondence. IJCV, 10, 53–66. 41 Cox, I. and Hingorani, S. L. (1994). An efficient implementation and evaluation of Reid’s multiple hypothesis tracking algorithm for visual tracking. In ICPR94, Vol. 1, pp. 437442. 42. Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2, 8397. 
Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 
Concepts  Adorno  Grenz I 121 Concept/Adorno/Grenz: the synthetic moment of concepts proves the nonconceptual to be its content: "The nonconceptual, the indispensable to the concept... I 122 ...disavowes its beinginitself and changes it. "(Negative Dialektik^{(1)}, p. 139). >Syntheticity. Adorno XIII 213 Recognition/nature/Epicurus/Adorno: Problem: how do you bring this together: to teach at the same time the beinginitself of nature, which is thus independent of us and yet to accept our sensory perception as the source of all recognition? >Nature/Adorno, >World/Thinking, >Cognition, >Theory of Knowledge. Solution Epicurus: recognition is a relatively early model for what I designate with aporetic terms. >Epicurus. Aporetic concept/Adorno: such concepts are not formed because there are any facts directly corresponding to them, but the theorists look at them, because their otherwise motivated theories would remain in unresolved contradictions and escape the unification. >Unification, >Unity, >Identity/Adorno. 
A I Th. W. Adorno Max Horkheimer Dialektik der Aufklärung Frankfurt 1978 A II Theodor W. Adorno Negative Dialektik Frankfurt/M. 2000 A III Theodor W. Adorno Ästhetische Theorie Frankfurt/M. 1973 A IV Theodor W. Adorno Minima Moralia Frankfurt/M. 2003 A V Theodor W. Adorno Philosophie der neuen Musik Frankfurt/M. 1995 A VI Theodor W. Adorno Gesammelte Schriften, Band 5: Zur Metakritik der Erkenntnistheorie. Drei Studien zu Hegel Frankfurt/M. 1071 A VII Theodor W. Adorno Noten zur Literatur (I  IV) Frankfurt/M. 2002 A VIII Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 2: Kierkegaard. Konstruktion des Ästhetischen Frankfurt/M. 2003 A IX Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 8: Soziologische Schriften I Frankfurt/M. 2003 A XI Theodor W. Adorno Über Walter Benjamin Frankfurt/M. 1990 A XII Theodor W. Adorno Philosophische Terminologie Bd. 1 Frankfurt/M. 1973 A XIII Theodor W. Adorno Philosophische Terminologie Bd. 2 Frankfurt/M. 1974 A X Friedemann Grenz Adornos Philosophie in Grundbegriffen. Auflösung einiger Deutungsprobleme Frankfurt/M. 1984 
Disjunction  Lewis  V 191 Disjunction/Lewis: each event can be described as a disjunction. But Goortalk that cannot be taken as the cause. >Events/Lewis. V 212 Disjunction/events/Lewis: an event cannot be disjunction of events, because then the (double) event would always have to occur in every region where one or the other disjunct occurs, i.e. it would have to happen twice in a possible world  impossible. V 263 Events are not disjunctive when different definitions are possible, but then the event still does not consist in their disjunction. V 266 Definition disjunction of events/Lewis: e is a disjunction of events f1, f2 .. iff. e necessarily occurs in a region, iff. either f1 or f2 or... occurs here  as a set the disjunction is simply the unification  some events are disjunctions of other events. E.g. stamping  of left and right foot  the disjuncts must not vary too much  the disjunctions as a whole are in noncausal counterfactual dependence on their referents, the disjuncts  without one, the whole thing would not be  but the individual disjuncts are not in this counterfactual dependence. Disjunction: not based on different definitions. E.g. Disposition: Breaking of the window could be caused by many things  none of the possible events is significantly associated with the fragility  if there are no extrinsic disjunctive events, there may still be disjunctive truths  and this can be explained causally. >Disposition/Lewis. 
Lewis I David K. Lewis Die Identität von Körper und Geist Frankfurt 1989 Lewis I (a) David K. Lewis An Argument for the Identity Theory, in: Journal of Philosophy 63 (1966) In Die Identität von Körper und Geist, Frankfurt/M. 1989 Lewis I (b) David K. Lewis Psychophysical and Theoretical Identifications, in: Australasian Journal of Philosophy 50 (1972) In Die Identität von Körper und Geist, Frankfurt/M. 1989 Lewis I (c) David K. Lewis Mad Pain and Martian Pain, Readings in Philosophy of Psychology, Vol. 1, Ned Block (ed.) Harvard University Press, 1980 In Die Identität von Körper und Geist, Frankfurt/M. 1989 Lewis II David K. Lewis "Languages and Language", in: K. Gunderson (Ed.), Minnesota Studies in the Philosophy of Science, Vol. VII, Language, Mind, and Knowledge, Minneapolis 1975, pp. 335 In Handlung, Kommunikation, Bedeutung, Georg Meggle Frankfurt/M. 1979 Lewis IV David K. Lewis Philosophical Papers Bd I New York Oxford 1983 Lewis V David K. Lewis Philosophical Papers Bd II New York Oxford 1986 Lewis VI David K. Lewis Convention. A Philosophical Study, Cambridge/MA 1969 German Edition: Konventionen Berlin 1975 LewisCl Clarence Irving Lewis Collected Papers of Clarence Irving Lewis Stanford 1970 LewisCl I Clarence Irving Lewis Mind and the World Order: Outline of a Theory of Knowledge (Dover Books on Western Philosophy) 1991 
Explanation  Goodman  IV 165 Explanation: a basic term is not defined, but explained by means of its different varieties. >Definitions.  II 67 Reduction sentences/Carnap: if we want to construct a language of science, we must take some descriptive (i.e. not logical) expressions as basic expressions. Other expressions can then be introduced by means of reduction sentences. >Reduction, >Reducibility, >Reductionism. II 68 GoodmanVsCarnap/reduction sentences: [the whole thing is] pretty absurd (...) in my opinion. Philosophy has the task of explaining the science (and the everyday language), but not describing it. The explanation must refer to the presystematic use of the terms under consideration, but does not have to adhere to the ordering. It is all about economy and unification. 
G IV N. Goodman Catherine Z. Elgin Reconceptions in Philosophy and Other Arts and Sciences, Indianapolis 1988 German Edition: Revisionen Frankfurt 1989 Goodman I N. Goodman Ways of Worldmaking, Indianapolis/Cambridge 1978 German Edition: Weisen der Welterzeugung Frankfurt 1984 Goodman II N. Goodman Fact, Fiction and Forecast, New York 1982 German Edition: Tatsache Fiktion Voraussage Frankfurt 1988 Goodman III N. Goodman Languages of Art. An Approach to a Theory of Symbols, Indianapolis 1976 German Edition: Sprachen der Kunst Frankfurt 1997 
Identity  Idealism  XIII 79 Identity/idealism/Adorno: I refer to the essence of idealism and also to the idealistic side in Kant as a thought of identity, that is, the thinking, which believes that everything that is can be deduced from a unified principle. >Principles, >Justification, >Ultimate Justification, >Foundation. XIII 80 What is decisive is that this uniform principle must always be a subject, even if it is also in a fluctuating sense. Identity thinking actually means to demand for as much as the primacy of subjectivity. >Unity, >Unification, >Subject, >Subjectivity. 

Inequalities  Aaron  Krastev I 35 Inequalities/Aron/Krastev: The globalization of communication has made the world a village, but this village is ruled by a dictatorship of global comparisons. People outside of North America and Western Europe no longer compare their lot only with that of their neighbours. They contrast their living standards with the living standards of the most prosperous inhabitants of the planet. The great French political philosopher and sociologist Raymond Aron presciently observed five decades ago that ‘with humanity on the way to unification, inequality between peoples takes on the significance that inequality between classes once had.’^{(1)} >Comparisons, >Comparability, >Globalization. 1. Raymond Aron, ‘The Dawn of Universal History’ in The Dawn of Universal History: Selected Essays from a Witness to the Twentieth Century (Basic Books, 2002), p. 482. 
Krastev I Ivan Krastev Stephen Holmes The Light that Failed: A Reckoning London 2019 
Intersubjectivity  Flusser  I 213 "Intersubjectivity"/Flusser: leads to say that a statement is the more true the more positions are expressed in it. The search for truth ceases to be epistemological and becomes "religious" in a new, yet unknown sense. >Perspective, >Truth, >Objectivity, >Subjectivity, >Society, >Unification. Point of view: diversity. Not indifference, but the insight that each point of view projects a specific value. >Aspects, cf. >Relativism, >Cultural relativism. 
Fl I V. Flusser Kommunikologie Mannheim 1996 
Lawlikeness  Schurz  I 237 Laws of nature/natural laws/Schurz: Laws of nature do not refer to specific physical systems but express what is valid for any systems in all physically possible universes. E.g. Newton's nuclear axioms (E.g. total force = mass times acceleration, E.g. force = counterforce, E.g. gravitational force is proportional to the product of masses). Only if they are used system conditions, which explicitly list the present forces, we get a concretely solvable differential equation. There are only a few fundamental ones and they are found only in physics. However, most of the laws of physics are: Def system laws/Schurz: involve concrete contingent system conditions. Therefore they are not physically necessary but contingent. Example law of fall, example law of pendulum, example law of planets etc. Lawlikeness/lawlike/Schurz: a) in the broad sense: the lawlike character of spatiotemporally limited general propositions is gradual. In this sense not only the laws of nature but also all system laws are lawlike. Counterfactual conditionals: if we would agree to them are an indication of lawlikeness. Problem: the counterfactual conditional also characterizes spatiotemporally bounded laws Ex "All ravens are black". Counterfactual conditionals/Schurz: on the other hand: we would not say Ex "If this apple had not been in the basket, it would not be green." >Counterfactual conditionals, >Laws of nature, >Laws. I 237 Similaritymetris/Possible Worlds/Counterfactual Conditional/RescherVsLewis/Schurz: (Lewis 1973b^{(1)}): for philosophy of science, Lewis' logical semantics for counterfactual conditionals yields little, because the substantive interpretation of the similarity metric between Possible Worlds presupposes that we already know a distinction between laws and contingent facts. (Stegmüller 1969^{(2)}, 320334). I 238 Lawlike/lawlike/Schurz: b) in the narrower sense: = physical necessity (to escape the vagueness resp. graduality of the broad term). Problem: Not all spatiotemporally unrestricted laws are lawlike in the narrow sense. Universal but not physically necessary: Ex "No lump of gold has a diameter of more than one kilometer". Universality: is not a sufficient, but a necessary condition for lawlikeness. E.g. the universal proposition "All apples in this basket are red" is not universal, even if one replaces it by its contraposition: Ex "All nonred objects are not apples in this basket". (Hempel 1965^{(3)}, 341). Strong Humethesis/Hume/Schurz: universality is a sufficient condition for lawlikeness. SchurzVs: this is wrong WeakHume thesis/Schurz: universality is a necessary condition for lawlikeness. >Causality/Hume. Stronger/weaker/(s): the claim that a condition is sufficient is stronger than that it is necessary. BhaskarVWeak Humethesis. Solution/Carnap/Hempel: Def Maxwell conditional/lawlike: laws of nature or nomological predicates must not contain an analytic reference to particular individuals or spacetime points (spacetime points). This is much stronger than the universality condition. >Stronger/weaker. Ex "All emeralds are grue": is spatiotemporally universal, but does not satisfy Maxwell's condition. >Grueness. I 239 Laws of nature/Armstrong: Thesis: Laws of nature are implication relations between universals. Therefore no reference to individuals. >Laws of nature/Armstrong, >Causality/Armstrong. MaxwellConditioning/Wilson/Schurz: (Wilson 1979): represent a physical symmetry principle: i.e. laws of nature must be invariant under translation of their time coordinates and translation or rotation of their space coordinates. From this, conservation laws can be obtained. Symmetry principles/principles/Schurz: physical symmetry principles are not a priori, but depend on experience! >Symmetries/Feynman, >Symmetries/Kanitscheider. Maxwellcondition/Schurz: is too weak for lawlike character: e.g. "no lump of gold has a diameter of more than 1 km" also this universal theorem fulfills it. Lawlikeness/Mill/Ramsey/Lewis/Schurz: proposal: all those general propositions which follow from those theories which produce the best unification of the set of all true propositions. (Lewis 1973b^{(1)}, 73). Vs: problem: it remains unclear why one should not add the proposition Bsp "No lump of gold has a diameter of more than 1 km". Because many true singular propositions also follow from it. Solution/Schurz: we need a clear notion of physical possibility. Problem: we have no consistent demarcation of natural laws and system laws. 1. Lewis, D. (1973b). Counterfactuals. Oxford: Basil Blackwell 2. Stegmüller, W. (1969). Probleme und Resultate der Wissenschaftstheorie und Analytischen Philosophie. Band I:Wissenschaftliche Erklärung und Begründung. Berlin: Springer. 3. Hempel, C. (1965). Aspects of Scientific Explanation and other Essays in the Philosophy of Science, New York: Free Press. 
Schu I G. Schurz Einführung in die Wissenschaftstheorie Darmstadt 2006 
Parliamentary System  Kelsen  Brocker I 132 Parliamentary System/Kelsen: If the parliament is seen as a representative of the people, the latter is regarded as predetermined, since it is only just being organised into a unit capable of action through the work of the parliament and the parties. Ideologically, the idea of representation made sense in the struggle against autocracy and now turns against democracy, if, for example, the model of professional representation is derived from this idea. >Democracy/Kelsen. Kelsen's' assumption that the people do not exist politically before parliamentary unification (cf. >People/Kelsen) is also based on the simple observation that there have practically never been consensus decisions, that the population always differentiates its opinions according to majority and minority (or minorities) and that therefore unification can only be found in the form of compromise.^{(1)} Brocker I 135 KelsenVsSchmitt/KelsenVsSmend/Llanque: Kelsen is mainly seen as the author who can clearly be counted among the supporters of parliamentary democracy among the majority of democracycritical state teachers of the Weimar Republic (Groh 2010)^{(2)}. He has published sharp criticisms of opponents in this debate, including Rudolf Smend and Carl Schmitt. Some also consider Kelsen to be the clearest opponent of Schmitt (Diner/Stolleis 1999^{(3)}; Dreier 1999^{(4)}). KelsenVsRousseau: unlike Rousseau, who rejects parliamentarism (RosseauVsParlamentarismus), Kelsen explains parliamentarism as a form of division of labour. 1. Hans Kelsen, »Vom Wesen und Wert der Demokratie«, in: Archiv für Sozialwissenschaft und Sozialpolitik 47, 1920/1921, 5085 (Separatdruck: Tübingen 1920). Erweiterte Fassung: Hans Kelsen, Vom Wesen und Wert der Demokratie, Tübingen 1929 (seitenidentischer Nachdruck:Aalen 1981), S. 57 2. Kathrin Groh, Demokratische Staatsrechtslehrer in der Weimarer Republik. Von der konstitutionellen Staatslehre zur Theorie des modernen demokratischen Verfassungsstaates, Tübingen 2010 3. Dan Diner & Michael (Hg.) Hans Kelsen and Carl Schmitt. A Juxtaposition, Gerlingen 1999 4. Horst Dreier »The Essence of Democracy: Hans Kelsen and Carl Schmitt Juxtaposed«, in: Dan Diner/Michael Stolleis (Hg.), Hans Kelsen and Carl Schmitt. A Juxtaposition, Gerlingen 1999, 7179 Marcus Llanque, „Hans Kelsen, Vom Wesen und Wert der Demokratie“, in: Manfred Brocker (Hg.) Geschichte des politischen Denkens. Das 20. Jahrhundert. Frankfurt/M. 2018 
Brocker I Manfred Brocker Geschichte des politischen Denkens. Das 20. Jahrhundert Frankfurt/M. 2018 
Reason  Idealism  Adorno XIII 130 Reason/idealism/Adorno: the first transformation of the concept of reason in idealism had meant that the contents of consciousness were taken into reason, but reflected in the sense that they should be... XIII 131 ...moments of consciousness themselves. That is, that the contents are mediated in themselves through subjectivity. >Content, >Consciousness, >Identity/Idealism, >Subjectivity, >Subject/Idealism. Reason/mind/Kant/Adorno: reason is then the ability to create unity in the manifoldness  according to laws. >Laws, >Unity and multiplicity, >Unification, >Order, >Unity, >Apprehension, >Apperception. 
A I Th. W. Adorno Max Horkheimer Dialektik der Aufklärung Frankfurt 1978 A II Theodor W. Adorno Negative Dialektik Frankfurt/M. 2000 A III Theodor W. Adorno Ästhetische Theorie Frankfurt/M. 1973 A IV Theodor W. Adorno Minima Moralia Frankfurt/M. 2003 A V Theodor W. Adorno Philosophie der neuen Musik Frankfurt/M. 1995 A VI Theodor W. Adorno Gesammelte Schriften, Band 5: Zur Metakritik der Erkenntnistheorie. Drei Studien zu Hegel Frankfurt/M. 1071 A VII Theodor W. Adorno Noten zur Literatur (I  IV) Frankfurt/M. 2002 A VIII Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 2: Kierkegaard. Konstruktion des Ästhetischen Frankfurt/M. 2003 A IX Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 8: Soziologische Schriften I Frankfurt/M. 2003 A XI Theodor W. Adorno Über Walter Benjamin Frankfurt/M. 1990 A XII Theodor W. Adorno Philosophische Terminologie Bd. 1 Frankfurt/M. 1973 A XIII Theodor W. Adorno Philosophische Terminologie Bd. 2 Frankfurt/M. 1974 
Recognition  Epicurus  Adorno XIII 213 Recognition/nature/Epicurus/Adorno: Problem: how do you bring this together: to teach at the same time the beinginitself of nature, which is thus independent of us and yet to accept our sensory perception as the source of all recognition? Solution Epicurus: recognition is a relatively early model for what I designate with aporetic terms. Aporetic concept/Adorno: such concepts are not formed because there are any facts directly corresponding to them, but the theorists look at them, because their otherwise motivated theories would remain in unresolved contradictions and escape the unification. Cf.>Nature/Aristotle, >Nature/Plato, >Perception/Aristotle, >Perception/Eleatics, >Perception/Gorgias, cf. >Rationality, >Sencory impression, 
A I Th. W. Adorno Max Horkheimer Dialektik der Aufklärung Frankfurt 1978 A II Theodor W. Adorno Negative Dialektik Frankfurt/M. 2000 A III Theodor W. Adorno Ästhetische Theorie Frankfurt/M. 1973 A IV Theodor W. Adorno Minima Moralia Frankfurt/M. 2003 A V Theodor W. Adorno Philosophie der neuen Musik Frankfurt/M. 1995 A VI Theodor W. Adorno Gesammelte Schriften, Band 5: Zur Metakritik der Erkenntnistheorie. Drei Studien zu Hegel Frankfurt/M. 1071 A VII Theodor W. Adorno Noten zur Literatur (I  IV) Frankfurt/M. 2002 A VIII Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 2: Kierkegaard. Konstruktion des Ästhetischen Frankfurt/M. 2003 A IX Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 8: Soziologische Schriften I Frankfurt/M. 2003 A XI Theodor W. Adorno Über Walter Benjamin Frankfurt/M. 1990 A XII Theodor W. Adorno Philosophische Terminologie Bd. 1 Frankfurt/M. 1973 A XIII Theodor W. Adorno Philosophische Terminologie Bd. 2 Frankfurt/M. 1974 
Regression  Ricoeur  I 103 Regression/Freud/dream interpretation/Ricoeur: the aspects of regression are temporal, topical and dynamic. Ricoeur: What refers us  in this regression  from concepts of meaning to concepts of power is the "short circuit" between the archaic and the 'dreamlike'; because this fantasy is a fantasy of desire. If the dream is drawn to speech because of its narrative character, then I 104 his relationship to desire brings him back to the side of energy, conatus, desire, will to power, libido, or whatever you want to call it. Thus the dream, as an expression of desire, stands between sense and power.  Condensation and displacement: see >Overdetermination/Ricoeur, >Terminology/Freud, >Censorship/Ricoeur. I 106 I 106 Presenting/representation: during condensation and displacement from the falsification of the themes or "content", "presenting" refers to another aspect of regression, which Freud calls formal regression (as opposed to temporal regression (...) I 107 and topical regression). But this "presenting" (Darstellung) is suitable for a description in terms of meaning; thus one notices the syntactic collapse, the replacement of all logical relations by pictorial correspondences, the representation of negation (through the unification of opposites in a single object, the mimic or rebus character of the manifest content, as well as the regression to the imagined or concrete image in general; (...). Problem: "Consideration of representability" („Rücksicht auf Darstellbarkeit“)/Freud: What seems to characterize the dream in this respect is the regression to hallucinatory animation of perception, beyond the memory images. Freud can thus say: The fabric of dream thoughts is dissolved into its raw material during regression.^{(1)} Ricoeur: But this regression to the image, which we have described as a hallucinatory stimulation of perception, is at the same time an economic phenomenon that can only be expressed as "changes in the energy occupations of the individual systems"^{(2)}. >Presentation/Freud/Ricoeur. 1. S. Freud, GW II/III, 549. 2. Ibid. 
Ricoeur I Paul Ricoeur De L’interprétation. Essai sur Sigmund Freud German Edition: Die Interpretation. Ein Versuch über Freud Frankfurt/M. 1999 Ricoeur II Paul Ricoeur Interpretation theory: discourse and the surplus of meaning Fort Worth 1976 
SelfDetermination  Political Philosophy  Gaus I 259 Selfdetermination/Political Philosophy/Kukathas: in the nineteenth century, nationalism was allied with Gaus I 260 liberalism as the principle of nationality was invoked as a principle of freedom  and against alien rule. >Nationalism, >Liberalism. Mazzini: the liberalism of Mazzini, for example, advocated the unification of Italy as a national republic from which French, Austrian and Papal power was expelled. Mill: John Stuart Mill saw a common nationality as a prerequisite for (liberal) representative government. >J. St. Mill. Liberalism/nonliberalism: in this light, national selfdetermination might seem unproblematic, as an ideal both liberals and nonliberals alike might readily accept: liberals because they favour selfdetermination, and non liberals because they favour national community. Yet matters are not so straightforward. In the first instance, what is always, and inescapably, controversial is the issue of who is the 'self' that is entitled to selfdetermination. Even if people within a boundary are entitled to govern themselves, how is the boundary to be drawn: who is to be included and who is to be excluded (Barry, 1991^{(1)}; 2001^{(2)}: 137)? Culture/group membership: theorists such as Raz and Margalit (1990)(3) look to resolve the problem by tying group membership to culture, suggesting that 'encompassing groups' have a number of characteristics that give them a unity which enables them to mount claims to selfhood and therefore selfdetermination. Central to such groups is a common culture, but no less impor tant is the fact that people within them recognize each other as members and regard their membership as important for their own selfidentification. It is also important to recognize, however, that the right of selfdetermination can be enjoyed only by a group that is a majority in a territory (1990^{(3)}: 441). VsIndividualism: what Raz and Margalit reject, as an undesirable illusion, is the individualist principle of consent: 'It is undesirable since the more important human groupings need to be based on shared history, and on criteria of nonvoluntaristic (or at least not wholly contractarian) membership to have the value they have' (1990^{(3)}: 456). Consent/KukathasVsRaz/KukathasVsMargalit: yet it is difficult to see how consent can fail to play a significant role in any account of self determination if selfdetermination is to mean some thing more than the determination of the lives of some by the will of others. And many other theories of selfdetermination give a substantial role to consent as central to any account of political legitimacy. >Consent. Beran: among the most sustained defences of the importance of consent is that offered in the writings of Harry Beran, particularly in his defence of the right of secession s central to the legitimacy of the liberal state (Beran, 1984^{(4)}; 1987^{(5)}; but see also Green, 1988(6); and Simmons, 2001(7)) (...). >Political Secession. 1. Barry, Brian (1991) 'Selfgovernment revisited'. Democracy and Power. Oxford: Clarendon, 15686. 2. Barry, Brian (2001) Cultuæ and Equality: An Egalitarian Critique of Multiculturalism. Oxford: Polity. 3.Raz and Margalit 1990 4. Beran, Harry (1984) 'A liberal theory of secession'. Political Studies, 32:2131. 5. Beran, Harry (1987) The Consent Theory of Political Obligation. London: Croom Helm. 6. Green, Leslie (1988) The Authority of the State. Oxford: Oxford University Press. 7. Simmons, A. John (2001) Justification and Legitimacy: Essays on Rights and Obligations. Cambridge: Cambridge University Press. Kukathas, Chandran 2004. „Nationalism and Multiculturalism“. In: Gaus, Gerald F. & Kukathas, Chandran 2004. Handbook of Political Theory. SAGE Publications 
Gaus I Gerald F. Gaus Chandran Kukathas Handbook of Political Theory London 2004 
Sense  Weber  Habermas III 22 Sense/Rationality/Max Weber/Habermas: Weber's hierarchy of action terms is based on the type of purposedriven action, so that all other actions can be classified as specific deviations from this type. >Purpose rationality. Weber analyzes the method of understanding the sense in such a way that the more complex cases can be related to the borderline case of understanding purposerational action: The understanding of the subjectively successoriented action requires at the same time its objective evaluation (according to standards of correctness rationality). >Success, >Rightness, >Rationality. Habermas III 229 Sense/Weber/Habermas: the empirical and completely mathematically oriented world view develops in principle the rejection of any viewpoint that asks for a 'sense' at all of the inner world happening. Wherever rational empirical recognition has consistently carried out the deenchantment of the world and its transformation into a causal mechanism, tension finally emerges against the claims of the ethical postulate: that the world is a Godordered, thus a somehow ethically meaningful cosmos. ^{(1)} >Ethics, >Justification, >Ultimate justification. Habermas III 315 Sense/Rationality/Weber/Habermas: That the world as a cosmos meets the requirements of rational religious ethics, or has some 'meaning', had nothing more to do with religious recognition. The cosmos of natural causality and the postulated cosmos of ethical equal causality were incompatible. The intellect created an unbrotherly aristocracy of rational cultural heritage independent of all personal ethical qualities of human beings.^{(2)} >Protestant Ethics/Weber, Rationalization/Weber. HabermasVsWeber: this explanation of social rationalization is unsatisfactory: Weber fails to prove that a principledriven moral consciousness can only survive in religious contexts. Habermas III 335/336 Sense/Weber/Habermas: Weber, Thesis of the loss of meaning: in view of the rational laws of modern life orders, both the ethical and theoretical unification of the world  whether in the name of religion or in the name of science  is no longer possible. >Unity, >Unification. Weber sees (in reference to the late work of J. St. Mill) a new polytheism, an objective figure of an antagonism between impersonal value and life orders. ^{(3)} Habermas: this reflects the generationtypical experience of nihilism. >Nihilism. Habermas III 337 Habermas: Weber justifies the thesis of the loss of meaning in this way: reason itself splits into a plurality of value spheres and destroys its own universality. The individual should now try to create this unit, which objectively can no longer be produced, in the privacy of his/her own biography. >Value spheres. Habermas III 377 Sense/Weber/Habermas: Weber introduces "sense" as an (undefined) basic concept for defining action. Thus, Weber has no theory of meaning behind him, but an intentionalist theory of consciousness. I.e. he does not refer to linguistic understanding but to the opinions and intentions of a subject of action. Habermas III 378 So it is a matter of purposive action, not communication. Communication can then only be constructed secondarily with the help of a concept of intention. ^{(4)} 1. M. Weber, M. Weber, Die protestantische Ethik, (Ed) J. Winckelmann, Vol. 2, Hamburg 1972, p. 569. 2. M. Weber, Gesammelte Ausätze zur Religionssoziologie, Bd. I. 1963, p. 569. 3.M. Weber, Gesammelte Aufsätze zur Wissenschaftslehre, (Ed) J. Winckelmann, Tübingen 1968, p. 603f. 4.M. Weber, Wirtschaft und Gesellschaft, (Ed) J. Winckelmann, Tübingen 1964, p. 3. 
Weber I M. Weber The Protestant Ethic and the Spirit of Capitalism  engl. trnsl. 1930 German Edition: Die protestantische Ethik und der Geist des Kapitalismus München 2013 Ha I J. Habermas Der philosophische Diskurs der Moderne Frankfurt 1988 Ha III Jürgen Habermas Theorie des kommunikativen Handelns Bd. I Frankfurt/M. 1981 Ha IV Jürgen Habermas Theorie des kommunikativen Handelns Bd. II Frankfurt/M. 1981 
Sequences  Tarski  Berka I 463 Definition sequence of subclasses: a sequence whose elements are all classes that are included in a given class a. Definition kth element/Tarski: the only element which satisfies the formula xRk and a natural number, is called the kth link "Rk". Berka I 463 Diversity: "at most differing at kth position"/Taski: are two sequences R and S, when any two corresponding elements of these sequences, Ri and Si, are identical, at most with the exception of the kth element of Rk and Sk. Berka I 511 Definition sequence of individuals/Tarski (in semantic unification): the twodigit relations between individuals and natural numbers  these all belong to the same meaning category regardless of the number of elements (of the sequence not of relation) and also the class of these sequences, as opposed to relations with more digits.^{(1)} >Satisfaction/Tarski, >Satisfiability/Tarski, >Truth/Tarski, >Truth definition/Tarski. 1. A.Tarski, Der Wahrheitsbegriff in den formalisierten Sprachen, Commentarii Societatis philosophicae Polonorum. Vol. 1, Lemberg 1935 
Tarski I A. Tarski Logic, Semantics, Metamathematics: Papers from 192338 Indianapolis 1983 Berka I Karel Berka Lothar Kreiser Logik Texte Berlin 1983 
Set Theory  Bourbaki  Thiel I 308 Quantum theory: in Bourbaki it is never spoken of logicism, always only of the set theory. Sets are genuine mathematical objects, and they are not reducible to others (logic: classes). The concept of sets is an essential tool for the unification of mathematics. >Sets, >Set theory, >Classes, >Logic, >Unification. I 308/309 Set theory: as a fundamental discipline of mathematics: basic concepts such as relation and function are traced back to the concept of the set, by explicit definition. >Element relation, >Subset, >Relations, >Functions. Relation functions as a symmetrical or asymmetrical pairing for a twodigit relation. Sometimes we need means to express the order: ordered pairs. >Ordered pairs. I 310. Def Functions: unambiguous relations. 
T I Chr. Thiel Philosophie und Mathematik Darmstadt 1995 
Set Theory  Thiel  Thiel I 308 Set Theory: Bourbaki never talks about logicism, only about set theory. Sets are genuinely mathematical objects, not reducible to others (logic: classes). The set concept is an essential tool for the unification of mathematics. >Unification, >Generalization, >Generality. I 308/309 Set Theory: as a fundamental discipline of mathematics: Basic concepts such as relation and function are traced back to the concept of set by explicit definition. Relation as symmetrical or asymmetrical pair formation. Twodigit relation. >Relations. Sometimes we need means to express the order. Ordered pairs. Def I 310. Functions: Def: right unambiguous relations. If one presupposes the traceability of all higher types of numbers to the natural numbers once, one can also win these still settheoretically. >Reduction, >Reducibility, >Numbers, >Real numbers. I 311 The real question is a philosophical one and concerns the justification of the reductionist program behind everything. Thiel: whether even numbers as mathematical entities turn out to be sets still appears today to be one of the most important philosophical questions, despite all the logical dead ends into which the classical logizistic approach has fallen. >Mathematical entities, >Logic, >Ontology, >Platonism, cf. >Hartry Field. 
T I Chr. Thiel Philosophie und Mathematik Darmstadt 1995 
Spirit  Fichte  Adorno XIII 88 Spirit/Fichte/Adorno: according to Fichte, the moment of the spirit sets itself; thereby the spirit is determined as a process. (...) >Process, cf. >Process philosophy. For idealism, the decisive factor is that such differences between the spirit and the material of sensuous matter and form itself, do not appear as a given and existent and do not appear as a final antithesis, but as a produced, as a mediated, and as a set. >Mediation. Here, the spirit is interpreted as the simply setting and thus as the absolute, in which the contrast, as it appeared in its classical, dualistic form in Descartes, is itself understood as a produced. >Dualism, >R. Descartes. The fact that both (...) n appear to be posited by a single principle, it seems to find a final unity in this setting unity principle, namely... XIII 89 ...its unity as a process. >Unity, >Unification. 
Fichte I Johann Gottlieb Fichte Zur Politik, Moral und Philosophie der Geschichte Berlin 1971 A I Th. W. Adorno Max Horkheimer Dialektik der Aufklärung Frankfurt 1978 A II Theodor W. Adorno Negative Dialektik Frankfurt/M. 2000 A III Theodor W. Adorno Ästhetische Theorie Frankfurt/M. 1973 A IV Theodor W. Adorno Minima Moralia Frankfurt/M. 2003 A V Theodor W. Adorno Philosophie der neuen Musik Frankfurt/M. 1995 A VI Theodor W. Adorno Gesammelte Schriften, Band 5: Zur Metakritik der Erkenntnistheorie. Drei Studien zu Hegel Frankfurt/M. 1071 A VII Theodor W. Adorno Noten zur Literatur (I  IV) Frankfurt/M. 2002 A VIII Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 2: Kierkegaard. Konstruktion des Ästhetischen Frankfurt/M. 2003 A IX Theodor W. Adorno Gesammelte Schriften in 20 Bänden: Band 8: Soziologische Schriften I Frankfurt/M. 2003 A XI Theodor W. Adorno Über Walter Benjamin Frankfurt/M. 1990 A XII Theodor W. Adorno Philosophische Terminologie Bd. 1 Frankfurt/M. 1973 A XIII Theodor W. Adorno Philosophische Terminologie Bd. 2 Frankfurt/M. 1974 
Symmetries  Kanitscheider  I 277 Symmetries/Kanitscheider: electromagnetic interaction: quantum electrodynamics, symmetry U(1), Strong Interaction: Quantum Chromodynamics, Symmetry of Color SU(3) Weak WW: Group SU(2) First unification step: Salam/Weinberg: gauge theory with the group structure SU(2)xU(1) Georgi/Glashow: (1974) 'Unification of the electroweak with the strong interaction: GUT/Great Unifying Theory: fundamental symmetry SU(5), which as a subgroup is the product of the three original forces SU(3)cxSU(2)xU(1) with included. I 276 Symmetry breaking/Kanitscheider: the spontaneous symmetry breaking occurs when the symmetric solution is unstable with a symmetric basic law. This transitions the system into an asymmetric state that obscures the original symmetry of the law. A broken symmetry is epistemically a hidden symmetry. I 279 Symmetry/Kanitscheider: a completely isotropic liquid can become anisotropic because of its crystalline character. Permanent magnets lose the common alignment of all elemental north and south poles when heated. In general, a system has higher symmetries at high temperatures than at lower ones. 
Kanitsch I B. Kanitscheider Kosmologie Stuttgart 1991 Kanitsch II B. Kanitscheider Im Innern der Natur Darmstadt 1996 
Technocracy  Morozov  I 138 Technocracy/VsTechnocracy/Technocracy Criticism/Technology Criticism/Morozov: most critics of modern technocracy or technology refer to the ((s) assumed) arrogance of planners and reformers who are lacking experience with the actual lives of people in their habitats. According to these critics, thought and consideration are indispensable; even the most perfect algorithms will not make them superfluous. Examples are: Jane Jacob, I. Berlin, F. Hayek, K. Popper, M. Oakeshott. >F. A. Hayek, >K. Popper, >M. Oakeshott, >I. Berlin, >Technology. Literature: I 137 Urban planning/Jane Jacob: Jacob's critique of unimaginative urban planning: see Jane Jacobs, The Death and Life of Great American Cities (New York: Vintage, 1992); Isaiah Berlin: his critique of a "ProCrusteanism": a compulsive unification: See Jonathan Allen, "Isaiah Berlin's AntiProcrustean Liberalism: Ideas, Circumstances, and the Protean Individual", lecture at the annual meeting of the American Political Science Association (2831 August 2003, Philadelphia, PA). Available at http:// berlin. wolf. ox. ac. ac. uk/ lists/ onib/ allen2003. pdf; Planning/Central Planning/Friedrich Hayek: his criticism of centralized planning: see Friedrich Hayek. The Use of Knowledge in Society", The American Economic Review 35, No. 4 (September 1, 1945): 519 530; Karl PopperVsHistorism: see Karl Popper. The Poverty of Historicism, I, Economica 11, No. 42 (May 1,1944): 86 103; Michael OakeshottVsRationalism: see Michael Oakeshott, Rationalism in Politics and other essays, exp. Edited by (Indianapolis: Liberty Fund, 1991). I 168 Definition Technoneutral/Majid Tehranian/Morozov: are preferably consultants who do not want to upset their clients. ^{(1)} I 170 Definition Technostructuralists/Tehranian/Morozov: believe that technologies evolve from institutional needs, spread by social forces of which they are part. ^{(2)} 1. Majid Tehranian, Technologies of Power: Information Machines and Democratic Prospects (New York: Ablex Publishing, 1990), 5. 2. ibid. 
Morozov I Evgeny Morozov To Save Everything, Click Here: The Folly of Technological Solutionism New York 2014 
Theories  Hacking  I 292 Theory/Hacking: I have no idea what a theory of "nondistortion through exposure to air" would be like. Observation without theory: e.g. Herrschel’s discovery of heat radiation 1800. >Discoveries, >Observation, >Seeing, >Method, >Science. I 291 ff His first assumption was that which we believe today. His theory was then entirely aligned to Newton, but that did not affect his observation. Problem: his observation was burdened by absolutely inadequate accuracy claims (precision, accuracy). He measured down to the thousandth, something which he was not able to do! The absence of a theory made him notice something: invisible infrared had to be included in the white light. (Hanson would have claimed that we would only be able to notice such a thing if we previously had a theory). >Theory ladenness. I 348 Unit/theory/Hacking: magnetism can affect light. Thus, it was possible to unify both (from interaction). >Unification, >Reduction. 1. Hanson, R. N. (1958). Patterns of discovery. Cambridge: Cambridge University Press 
Hacking I I. Hacking Representing and Intervening. Introductory Topics in the Philosophy of Natural Science, Cambridge/New York/Oakleigh 1983 German Edition: Einführung in die Philosophie der Naturwissenschaften Stuttgart 1996 
Unity  Feynman  I 116 Unification/Theory of Everything/TOE/Feynman: If one day we find a "universal equation", one of its roots could be this number 1/4,170000000000000.... If we compare the time required by light to travel through a proton to the age of the universe, the answer is 10^{42}. Thus it has the same number of zeros! So it was suggested that the gravitational constant is connected to the age of the world. But if it is connected, it would have to change over time! Vs: if that were the case, the world would have been 100° hotter at the time when life on it emerged, because it would have been closer to the sun. Life could not have developed. >Unification, >Life/Richard Dawkins, >Life/Stuart Kauffman, >Life/Ernst Mayr, >Life/Jacques Monod, cf. >Evolution, >Theory of Everything. 
Feynman I Richard Feynman The Feynman Lectures on Physics. Vol. I, Mainly Mechanics, Radiation, and Heat, California Institute of Technology 1963 German Edition: Vorlesungen über Physik I München 2001 Feynman II R. Feynman The Character of Physical Law, Cambridge, MA/London 1967 German Edition: Vom Wesen physikalischer Gesetze München 1993 
Unity  James  DiazBone I 128 Unity/Multiplicity/James: this is the most important philosophical problem, because it has substantive consequences. Is multiplicity really irrelevant? Unity is not the only need. Nevertheless, I will always regard unity as a preeminence to multiplicity. I 130 Unity: the concept alone cannot be a guarantee for the comprehension of the whole through the conceptual idea. The term universe is not an evidence for its actual existence. World/James: instead of the question of unity and multiplicity, the view is of particular importance that it is a spacetime continuum. I 131 Unity and multiplicity are absolutely equivalent here! I 131 Causality/James: can be spoken of causal unity or a purposeunit of the world at all? Multiplicity can be regarded just as eternal as causal unification! I 132 World/James: Neither universe nor multiverse, unity and multiplicity can exist simultaneously and side by side. The world is one in which their parts are connected. It is more and more brought into uniform systems by humanity. (Davidson: descriptiondependent; >Reality/Davidson, Descriptions/Davidson, Ontology/Davidson, World/Thinking/Davidson). 
James I R. DiazBone/K. Schubert William James zur Einführung Hamburg 1996 
Unity  Simons  I 326 Unity/Simons: unity is always in relation to something. >Absoluteness, >Unification, >Wholes, >Totality, >Dependence, >Independence. 
Simons I P. Simons Parts. A Study in Ontology Oxford New York 1987 
World  James  DiazBone I 128 World/Unity/Multiplicity/James: this is the most important philosophical problem, because it has substantive consequences. Is multiplicity really irrelevant? Unity is not the only need. Nevertheless, I will always regard unity as a preeminence to multiplicity. I 130 Unity: the concept alone cannot be a guarantee for the comprehension of the whole by the conceptual idea. The term universe is not an evidence for its actual existence. World/James: instead of the question of unity and multiplicity, the view is of particular importance that it is a spacetime continuum. I 131 Unity and multiplicity are absolutely equivalent here. Causality/James: can then be spoken of causal unity or a purposeunit of the world at all? Multiplicity can be regarded just as eternal as causal unification. I 132 World/James: Neither universe nor multiverse, unity and multiplicity can exist simultaneously and side by side. The world is one in which their parts are connected. It is more and more brought into uniform systems by humanity. (Davidson: descriptiondependent; >Reality/Davidson, Descriptions/Davidson, Ontology/Davidson, World/Thinking/Davidson). 
James I R. DiazBone/K. Schubert William James zur Einführung Hamburg 1996 
Disputed term/author/ism  Author Vs Author 
Entry 
Reference 

Deduction  Dummett Vs Deduction  I 96 Explanation: DuhemVsFraassen: unification merely fictitious assumption to simplify  DuhemVsDeduction  DuhemVsDeductiveNomological Model  FraassenVsDuhem: the empirical substructure of the theory should be isomorphic to that of the phenomena DuhemVsFraassen: that’s only very roughly possible  (Cartwright ditto). 
Dummett I M. Dummett The Origins of the Analytical Philosophy, London 1988 German Edition: Ursprünge der analytischen Philosophie Frankfurt 1992 Dummett II Michael Dummett "What ist a Theory of Meaning?" (ii) In Truth and Meaning, G. Evans/J. McDowell Oxford 1976 Dummett III M. Dummett Wahrheit Stuttgart 1982 Dummett III (a) Michael Dummett "Truth" in: Proceedings of the Aristotelian Society 59 (1959) pp.141162 In Wahrheit, Michael Dummett Stuttgart 1982 Dummett III (b) Michael Dummett "Frege’s Distiction between Sense and Reference", in: M. Dummett, Truth and Other Enigmas, London 1978, pp. 116144 In Wahrheit, Stuttgart 1982 Dummett III (c) Michael Dummett "What is a Theory of Meaning?" in: S. Guttenplan (ed.) Mind and Language, Oxford 1975, pp. 97138 In Wahrheit, Michael Dummett Stuttgart 1982 Dummett III (d) Michael Dummett "Bringing About the Past" in: Philosophical Review 73 (1964) pp.338359 In Wahrheit, Michael Dummett Stuttgart 1982 Dummett III (e) Michael Dummett "Can Analytical Philosophy be Systematic, and Ought it to be?" in: HegelStudien, Beiheft 17 (1977) S. 305326 In Wahrheit, Michael Dummett Stuttgart 1982 
Fichte, J.G.  Nozick Vs Fichte, J.G.  II 87 Synthesis/I/Self/Nozick: which principle should regulate how it works? What creates an array around the original pointshaped act A? Reflexive SelfReference: Act A has its own aspects: 1) It is an intentional action expressed in the physical production of a sound or character II 88 And 2) localized somewhere, etc. Then again, certain aspects might stand out in the classification, as above. What is it that performs the synthesis? Does the classification principle exist independently of me, the subject? Or is my self synthesized? Who or what then performs the synthesis? Act: 1) let us first imagine acts without agents, then we have a series A1 ... An. These include (but are not limited to) acts that fulfil the schema of the next relation, that unite entities in classification and synthesize them. II 89 2) Now let us imagine a different act A0 of unification and synthesis which brings together A1 ... An and also A0 itself. Form of thought: insertion. A0 is (partly) an act of reflexive selfreference. The act of synthesis of A1 ... An. A0 unites them as parts of the same entity E. Agent view: although there are ways of combining acts without agents, we want to take the "agent view" here. Then, the entity E is the agent of these acts, including A0. If E already preexists independently, it is easy to understand. Then A0 only draws the borderline around E. Synthesis: but if we take the concept more seriously: can we say (afterwards) that what A0 did was the entity E synthesized by A0 itself? I/NozickVsFichte: can the rabbit be pulled out of the rabbit? That would be a Fichtean theory: the self sets itself as setting itself. That seems bizarre, if not incoherent. But otherwise we would have to assume a preexisting self and in turn ask about the origin. Synthesis: an ongoing synthesis does not accurately determine the character of a subsequent synthesis, even if it is forwardlooking, but it can cause what happens later. Nevertheless, the same type of synthesis can, if there are no obstacles, result in a continuous entity. ((s) Vs: How does Nozick know that?). Nozick: there is not a new creative act of synthesis necessary every time when referring to yourself. Nozick: not every act has to redraw the boundaries or involve the drawing of boundaries. One can assume former borders. II 90 Nozick: because the outline of the self in the synthesis is performed according to the principles of classification and entification, like previous syntheses, it is not accidental. That may nourish the illusion of a preexisting entity. 
No I R. Nozick Philosophical Explanations Oxford 1981 No II R., Nozick The Nature of Rationality 1994 
Realism  Duhem Vs Realism  Cartwright I 76 DuhemVsRealism: uses the argument of redundancy: also PutnamVsRealism: for every explanation of any amount of data, there is always an alternative. Cartwright: both do not distinguish between causal explanations and theoretical explanations. Cartwright. I 95 Nature/Duhem/Cartwright: Duhem thesis: the phenomena of nature disintegrate roughly in natural species. DuhemVsRealism: there is no unification. It’s just a raw fact that some things can sometimes behave like certain other things. And that can be an indication of the behavior of other things. Explanation/Duhem: draws up a scheme that allows the use of these indications. Unification/Duhem/Cartwright: is only fictional: E.g. It’s easier for us to postulate Maxwell’s four laws and an electromagnetic field in order to display both light and electricity as a manifestation of a single property, but the unification itself does not exist. The phenomena are also completely different! Truth/Explanation/Duhem/Cartwright: We cannot expect to find a explanatory law for two different phenomena, which is also true. 
Duh I P. Duhem La théorie physique, son objet et sa structure, Paris 1906 German Edition: Ziel und Struktur der physikalischen Theorien Hamburg 1998 Car I N. Cartwright How the laws of physics lie Oxford New York 1983 CartwrightR I R. Cartwright A Neglected Theory of Truth. Philosophical Essays, Cambridge/MA pp. 7193 In Theories of Truth, Paul Horwich Aldershot 1994 CartwrightR II R. Cartwright Ontology and the theory of meaning Chicago 1954 
Reductionism  Quine Vs Reductionism  Davidson I 89 QuineVsreductionism: Quine appeals to today’s science as the best theory of our world. The irritations of our sense organs are the only evidence of "operations in their environment." Of course, this is not reductionism. Davidson II 130 2nd Dogma: reductionism: the view that every meaningful statement is equivalent to a logical construction of terms which refer to immediate experience. Quine IV 412 Def Reductionism (radical form)/Quine: according to him, every single meaningful expression is translatable into an expression about immediate experience. QuineVsReductionism: radical form: false translatability of individual observations into individual expressions. >HolismVs. weaker form: continued idea: a particular area of sensory irritation is clearly assigned to any (synthetic) statement. (Falsely). Vs: responses to sensory stimuli are not rigid in humans. Rorty I 241 QuineVsReductionism/Rorty: before Quine theorists made a significant contribution to the unification of science. After Quine's attacks on the concept of meaning there is the need to replace functional descriptions of theoretical entities with structural descriptions. (speaking of DNA molecules instead of genes). 
Quine I W.V.O. Quine Word and Object, Cambridge/MA 1960 German Edition: Wort und Gegenstand Stuttgart 1980 Quine II W.V.O. Quine Theories and Things, Cambridge/MA 1986 German Edition: Theorien und Dinge Frankfurt 1985 Quine III W.V.O. Quine Methods of Logic, 4th edition Cambridge/MA 1982 German Edition: Grundzüge der Logik Frankfurt 1978 Quine V W.V.O. Quine The Roots of Reference, La Salle/Illinois 1974 German Edition: Die Wurzeln der Referenz Frankfurt 1989 Quine VI W.V.O. Quine Pursuit of Truth, Cambridge/MA 1992 German Edition: Unterwegs zur Wahrheit Paderborn 1995 Quine VII W.V.O. Quine From a logical point of view Cambridge, Mass. 1953 Quine VII (a) W. V. A. Quine On what there is In From a Logical Point of View, Cambridge, MA 1953 Quine VII (b) W. V. A. Quine Two dogmas of empiricism In From a Logical Point of View, Cambridge, MA 1953 Quine VII (c) W. V. A. Quine The problem of meaning in linguistics In From a Logical Point of View, Cambridge, MA 1953 Quine VII (d) W. V. A. Quine Identity, ostension and hypostasis In From a Logical Point of View, Cambridge, MA 1953 Quine VII (e) W. V. A. Quine New foundations for mathematical logic In From a Logical Point of View, Cambridge, MA 1953 Quine VII (f) W. V. A. Quine Logic and the reification of universals In From a Logical Point of View, Cambridge, MA 1953 Quine VII (g) W. V. A. Quine Notes on the theory of reference In From a Logical Point of View, Cambridge, MA 1953 Quine VII (h) W. V. A. Quine Reference and modality In From a Logical Point of View, Cambridge, MA 1953 Quine VII (i) W. V. A. Quine Meaning and existential inference In From a Logical Point of View, Cambridge, MA 1953 Quine VIII W.V.O. Quine Designation and Existence, in: The Journal of Philosophy 36 (1939) German Edition: Bezeichnung und Referenz In Zur Philosophie der idealen Sprache, J. Sinnreich (Hg) München 1982 Quine IX W.V.O. Quine Set Theory and its Logic, Cambridge/MA 1963 German Edition: Mengenlehre und ihre Logik Wiesbaden 1967 Quine X W.V.O. Quine The Philosophy of Logic, Cambridge/MA 1970, 1986 German Edition: Philosophie der Logik Bamberg 2005 Quine XII W.V.O. Quine Ontological Relativity and Other Essays, New York 1969 German Edition: Ontologische Relativität Frankfurt 2003 Quine XIII Willard Van Orman Quine Quiddities Cambridge/London 1987 Davidson I D. Davidson Der Mythos des Subjektiven Stuttgart 1993 Davidson I (a) Donald Davidson "Tho Conditions of Thoughts", in: Le Cahier du Collège de Philosophie, Paris 1989, pp. 163171 In Der Mythos des Subjektiven, Stuttgart 1993 Davidson I (b) Donald Davidson "What is Present to the Mind?" in: J. Brandl/W. Gombocz (eds) The MInd of Donald Davidson, Amsterdam 1989, pp. 318 In Der Mythos des Subjektiven, Stuttgart 1993 Davidson I (c) Donald Davidson "Meaning, Truth and Evidence", in: R. Barrett/R. Gibson (eds.) Perspectives on Quine, Cambridge/MA 1990, pp. 6879 In Der Mythos des Subjektiven, Stuttgart 1993 Davidson I (d) Donald Davidson "Epistemology Externalized", Ms 1989 In Der Mythos des Subjektiven, Stuttgart 1993 Davidson I (e) Donald Davidson "The Myth of the Subjective", in: M. Benedikt/R. Burger (eds.) Bewußtsein, Sprache und die Kunst, Wien 1988, pp. 4554 In Der Mythos des Subjektiven, Stuttgart 1993 Davidson II Donald Davidson "Reply to Foster" In Truth and Meaning, G. Evans/J. McDowell Oxford 1976 Davidson III D. Davidson Essays on Actions and Events, Oxford 1980 German Edition: Handlung und Ereignis Frankfurt 1990 Davidson IV D. Davidson Inquiries into Truth and Interpretation, Oxford 1984 German Edition: Wahrheit und Interpretation Frankfurt 1990 Davidson V Donald Davidson "Rational Animals", in: D. Davidson, Subjective, Intersubjective, Objective, Oxford 2001, pp. 95105 In Der Geist der Tiere, D Perler/M. Wild Frankfurt/M. 2005 Rorty I Richard Rorty Philosophy and the Mirror of Nature, Princeton/NJ 1979 German Edition: Der Spiegel der Natur Frankfurt 1997 Rorty II Richard Rorty Philosophie & die Zukunft Frankfurt 2000 Rorty II (b) Richard Rorty "Habermas, Derrida and the Functions of Philosophy", in: R. Rorty, Truth and Progress. Philosophical Papers III, Cambridge/MA 1998 In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty II (c) Richard Rorty Analytic and Conversational Philosophy Conference fee "Philosophy and the other hgumanities", Stanford Humanities Center 1998 In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty II (d) Richard Rorty Justice as a Larger Loyalty, in: Ronald Bontekoe/Marietta Stepanians (eds.) Justice and Democracy. Crosscultural Perspectives, University of Hawaii 1997 In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty II (e) Richard Rorty Spinoza, Pragmatismus und die Liebe zur Weisheit, Revised Spinoza Lecture April 1997, University of Amsterdam In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty II (f) Richard Rorty "Sein, das verstanden werden kann, ist Sprache", keynote lecture for Gadamer’ s 100th birthday, University of Heidelberg In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty II (g) Richard Rorty "Wild Orchids and Trotzky", in: Wild Orchids and Trotzky: Messages form American Universities ed. Mark Edmundson, New York 1993 In Philosophie & die Zukunft, Frankfurt/M. 2000 Rorty III Richard Rorty Contingency, Irony, and solidarity, Chambridge/MA 1989 German Edition: Kontingenz, Ironie und Solidarität Frankfurt 1992 Rorty IV (a) Richard Rorty "is Philosophy a Natural Kind?", in: R. Rorty, Objectivity, Relativism, and Truth. Philosophical Papers Vol. I, Cambridge/Ma 1991, pp. 4662 In Eine Kultur ohne Zentrum, Stuttgart 1993 Rorty IV (b) Richard Rorty "NonReductive Physicalism" in: R. Rorty, Objectivity, Relativism, and Truth. Philosophical Papers Vol. I, Cambridge/Ma 1991, pp. 113125 In Eine Kultur ohne Zentrum, Stuttgart 1993 Rorty IV (c) Richard Rorty "Heidegger, Kundera and Dickens" in: R. Rorty, Essays on Heidegger and Others. Philosophical Papers Vol. 2, Cambridge/MA 1991, pp. 6682 In Eine Kultur ohne Zentrum, Stuttgart 1993 Rorty IV (d) Richard Rorty "Deconstruction and Circumvention" in: R. Rorty, Essays on Heidegger and Others. Philosophical Papers Vol. 2, Cambridge/MA 1991, pp. 85106 In Eine Kultur ohne Zentrum, Stuttgart 1993 Rorty V (a) R. Rorty "Solidarity of Objectivity", Howison Lecture, University of California, Berkeley, January 1983 In Solidarität oder Objektivität?, Stuttgart 1998 Rorty V (b) Richard Rorty "Freud and Moral Reflection", Edith Weigert Lecture, Forum on Psychiatry and the Humanities, Washington School of Psychiatry, Oct. 19th 1984 In Solidarität oder Objektivität?, Stuttgart 1988 Rorty V (c) Richard Rorty The Priority of Democracy to Philosophy, in: John P. Reeder & Gene Outka (eds.), Prospects for a Common Morality. Princeton University Press. pp. 254278 (1992) In Solidarität oder Objektivität?, Stuttgart 1988 Rorty VI Richard Rorty Truth and Progress, Cambridge/MA 1998 German Edition: Wahrheit und Fortschritt Frankfurt 2000 
Disputed term/author/ism  Pro/Versus 
Entry 
Reference 

deductive nomolog.  Versus  I 96 Explanation: DuhemVsFraassen: Unification merely fictitious assumption to simplify  DuhemVsDeduction  deductivenomological model DuhemVs  FraassenVsDuhem: the empirical substructure of theory should be isomorphic to the phenomena  DuhemVsFraassen: that is more than very roughly  (Cartwright ditto). 

Disputed term/author/ism  Author 
Entry 
Reference 

Nature  Duhem, P.  Cartwright I 94 Nature/Duhem/Cartwright: Duhem thesis: the phenomena of nature decay roughly speaking into natural species. DuhemVsRealism: there is no union. It's just a raw fact that some things sometimes behave like certain other things. And this can be an indication of the behavior of other things. Explanation/Duhem: sets up a scheme that allows these clues to be used. Unification/Duhem/Cartwright: is only fictitious: For example, it is easier for us to postulate Maxwell's four laws and one electromagnetic field to see both light and electricity as manifestations of a single characteristic, but the unification itself does not exist. The phenomena are very different! Truth/Explanation/Duhem/Cartwright: we cannot expect to find an explanatory law for two different phenomena that is also true! 
Car I N. Cartwright How the laws of physics lie Oxford New York 1983 CartwrightR II R. Cartwright Ontology and the theory of meaning Chicago 1954 
Computation  Fodor, J.  Fodor/Lepore IV 126 Computation/Fodor/Lepore: thesis: the causal role of representations is determined by the same syntactic properties on which their compositionality depends. IV 179 Computation/Fodor/Lepore: thesis: causal relations reconstruct inferential relations. The hope of a unification of semantics and psychology is connected with this. Pauen I 147 Computation/JohnsonLaird: thesis, once one understands how a computer works, the mind can be examined independently of the brain. Pauen I 148 The meaning differences of "0" and "1" correspond to physical differences of the switching states. In the same way one must imagine the effectiveness of meaningful states in the cognitive system of a human being. Fodor: in contrast to this (computer) neuronal networks function completely differently, namely associatively. 
Pauen I M. Pauen Grundprobleme der Philosophie des Geistes Frankfurt 2001 