## TOPICS OF REFEREED PUBLICATIONS:

**Human Behavior and Control of Collective Systems**

**Statistical Inference and Machine Learning**

**Physics and Computation**

**Optimization and Search**

**Other topics**

*Detailed list:*

**Human Behavior and Control of Collective Systems**

*REFEREED PUBLICATIONS TOPICS:*

**Human Behavior and Control of Collective Systems**

**Statistical Inference and Machine Learning**

**Physics and Computation**

**Optimization and Search**

**Other topics**

*Detailed list:*

**Human Behavior and Control of Collective Systems**

Backhaus, S., Bent, R., Bono, J., Lee, R, Tracey, B., Wolpert, D.h., Xie, D. and Yildiz, Y., “Cyber-Physical Security: A Game Theory Model of Humans Interacting

over Control Systems”,

*IEEE Transactions on Smart Grid*, in press.

Wolpert, D.H., and Bono, J., “A theory of unstructured bargaining using distribution-valued solution concepts”,

*Journal of Artificial Intelligence Research,*in press.

Yan, G., Lee, R., Kent, A, Wolpert, D., “Towards a Bayesian network game framework for evaluating DDoS attacks and defense”, Proceedings of

*2012 ACM Conference on Computer and Communications Security*, in press.

Schlicht E., Lee R., Tracey B., Wolpert, D., Kochenderfer M., “Predicting the behavior of interacting humans by fusing data from multiple sources”,

*Uncertainty in Artificial Intelligence 2012*, K. Murphy (Ed.), in press.

Lee, R., Wolpert, D.H., Backhaus, S. Bent, R., Bono, J., Tracey, B., “Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate the Future”,

*Decision-Making with Imperfect Decision Makers 2012*, T. Guy, M. Karny and D.H.Wolpert (Ed.’s), Springer, in press.

Wolpert, D.H., and Harre, M., and Bertschinger, N., and Olbrich, E., and Jost, J., “Hysteresis effects of changing parameters of noncooperative games”,

*Physical Review E,*

**85**, 036102

**,**2012.

Wolpert, D. H. and Leslie, D. “Information Theory and Observational Limitations in Decision Making”,

*Berkeley Electronic Journal of Theoretical Economics*, 2011.

Wolpert, D. H. and Jamison, J. “The Strategic Choice of Preferences: the Persona Model”,

*Berkeley Electronic Journal of Theoretical Economics*, 2011.

Wolpert, D.H. and Bono, J.W., “Distribution-valued Solutions Concepts”,

*Games and Economic Behavior*, accepted subject to revision.

Lee, R. and Wolpert, D.H., “Game-Theoretic Modeling of Human Behavior in Mid-Air Collisions”,

*Decision-Making with Imperfect Decision Makers 2011*, T. Guy, M. Karny and D.H.Wolpert (Ed.’s), Springer, in press.

Wolpert, D. H. and Jamison, J. “Schelling Formalized: Strategic Choices of Non-Rational Behavior”,

*Evolution and Rationality: Decisions, Cooperation, and Strategic Behavior*, K. Binmore and S. Okasha (Ed.’s), Cambridge University Press, in press.

Wolpert, David H. and Bono, J. W. “PGT: A Statistical Approach to Prediction and Mechanism Design

*”, Proc. of SBP 2010*, Sun-Ki Chai, John Salerno, and Patricia Mabry (Eds.), Springer 2010.

Wolpert, D.H., “Why Income Comparison is Rational”,

*Games and Economic Behavior*,

**69**, issue 2, 458-474, 2010.

Wolpert, D.H., “Trembling Hand Perfection for mixed Quantal / Best Response Equilibria”,

*International Journal of Game Theory*,

**8**, Issue 4, Page 539, 2009.

Wolpert, D.H. and Kulkarni, N., “Game-theoretic Management of Interacting Adaptive Systems”,

*Proc. 2008 NASA/ESA Conference on Adaptive Hardware and Systems*.

Wolpert, D.H., Strauss, C.E.M., Rajnarayan, D., “Advances in Distributed Optimization using Probability Collectives”,

*Advances in Complex Systems*,

**9**, 2006.

Lawson, J and Wolpert, D.H., “Adaptive Programming of Unconventional Nano-Architectures”,

*Journal of Computational and Theoretical Nanoscience*,

**3**, 272-279, 2006.

Bieniawski. S., Kroo, I., and Wolpert, D.H. “Flight Control with Distributed Effectors,” AIAA Paper 2005-6074, Proceedings of the 2005

*AIAA Guidance, Navigation, and Control Conference*, San Francisco, CA, August 15-18, 2005.

Wolpert, D.H., Bieniawski, S.R., “Distributed Control by Lagrangian Steepest Descent”, in

*Proceedings of IEEE Conference on Decision and Control*, 2004.

Wolpert, D.H., Huang, C.F, Bieniawski, S. and Strauss, C.E.M., “A comparative study of Probability Collectives-based Multi-agent Systems and Genetic Algorithms”,

*Proceedings of 2005 GECCO conference*.

Bieniawski, S., Kroo, I., and Wolpert, D. H., “Discrete, Continuous, and Constrained Optimization Using Collectives,” AIAA Paper 2004-4580, 10th AIAA/ISSMO

*Multi-disciplinary Analysis and Optimization Conference*, Albany, NY, August 30-September 1, 2004.

Wolpert, D.H., Bieniawski, S., “Distributed Adaptive Control: Beyond Single-Instant, Discrete Variables”, in MSRAS 04, Springer-Verlag, 2004.

Wolpert, D.H., “What Information Theory says about Bounded Rational Best Response”, in WEHIA 04, A. Namatame (Ed.), Springer-Verlag, 2004.

Bieniawski, S.R., Wolpert, D.H., “Adaptive, distributed control of constrained multi-agent systems”, in

*Autonomous Agents and Multi-Agent Systems*

*2004*, 2004.

Lee, C.F., Wolpert, D.H., “Product distribution theory for control of multi-agent systems”, in

*Autonomous Agents and Multi-Agent Systems*

*2004*, 2004.

Wolpert, D.H., Lee, C.F., “Adaptive Metropolis Sampling with Product Distributions”, in

*International Conference on Complex Systems 2004*, Y. Bar-Yam (Ed.), Perseus books, 2004.

Bieniawski, S.R., Wolpert, D.H., “Product Distributions for Distributed Optimization”, in

*International Conference on Complex Systems 2004*, Y. Bar-Yam (Ed.), Perseus books, 2004.

Macready, W., Wolpert, D.H., “Distributed Constrained Optimization”, in International

*Conference on Complex Systems 2004*, Y. Bar-Yam (Ed.), Perseus books, 2004.

Wolpert, D.H., “Information theory - the bridge connecting bounded rational game theory and statistical physics”, in

*Complex Engineering Systems*, D. Braha and Y. Bar-Yam (Ed.’s), Perseus books, 2004.

Tumer, K., Wolpert, D.H., “Coordination in Large Collectives”, in

*International Conference on Complex Systems 2004*, Y. Bar-Yam (Ed.), Perseus books, 2004.

Antoine, N.E., Bieniawski, S.R., Kroo, I.M., Wolpert, D.H., “Fleet Assignment using collective intelligence”, AIAA-2004-0622, Presented at the 42nd Aerospace Sciences Meeting, 2004.

Wolpert, D.H., Tumer, K., Bandari, E. “Improving search algorithms by using intelligent coordinates”,

*Physical Review E (Brief Communications),*

**69**, 017701, 2004.

Tumer, K., Wolpert, D.H., “A Survey of Collective Intelligence”, in Tumer, K., and Wolpert, D.H. (Ed.’s

*) Collectives and the Design of Complex Systems*, Springer-Verlag, 2004.

Wolpert, D.H., “The Theory of Collectives”, in Tumer, K., and Wolpert, D.H. (Ed.’s)

*Collectives and the Design of Complex Systems*, Springer-Verlag, 2004.

Airiau, S., Wolpert, D.H., Sen, S., and Tumer, K., “Providing effective access to shared resources: a COIN approach”,

*Proceedings of ESOA ‘03*, A. Karageorgos et al., 2003.

Wolpert, D.H., and Tumer, K., “Beyond Mechanism Design”,

*International Congress of Mathematicians 2002 Proceedings*, H. Gao et al. (Ed.s), Qingdao Publishing, 2002.

Lawson, J., and Wolpert D. H., “The Design of Collectives of Agents to Control Non-Markovian Systems”,

*Proceedings of American Association of Artificial Intelligence Conference 2002*, 2002.

Wolpert, D.H, and Lawson, J., “Designing Agent Collectives For Systems With Markovian Dynamics”, in

*Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems*, 2002.

Tumer, K., Agogino, A, and Wolpert, D.H., “Learning Sequences of Actions in Collectives of Autonomous Agents”, in

*Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems*, 2002.

Wolpert, D.H., and Tumer, K., “Optimal Reward Functions in Distributed Reinforcement Learning”,

*Intelligent Agent Technology*2001, 2002.

Wolpert, D.H., Tumer, K. “Collective Intelligence, Data Routing, and Braess’ Paradox”,

*Journal of Artificial Intelligence Research*, 2002.

Wolpert, D.H., “Collective Intelligence”,

*in Computational Intelligence Beyond*2001: Real and Imagined, D. Fogel and D. Robinson (Ed.), Wiley, 2001.

Wolpert, D., and Tumer, K., “Optimal Payoff Functions for Members of Collectives”,

*Advances in Complex Systems*, Vol. 4, pp. 265-280, 2001.

Wolpert, D.H., Sill, J., and Tumer, K., “Using Collective Intelligence to Control Data Flow Across a Constellation of Satellites”,

*Proceedings International Joint Conference on AI 2001*, Morgan Kauffman, 2001.

Wolpert, D.H., and Tumer, K., “An Illustration of the COIN Approach to Design of Multi-Agent Systems”,

*Proceedings of the Agents 00 and ECML 00 Workshop on Learning in Agents*, Sen. S et al. (Ed.’s), 2000.

Tumer, K., and Wolpert, D.H. “Collective Intelligence and Braess’ Paradox”, in

*Proceedings of AAAI 2000*, Morgan Kauffman, 2000.

Wolpert, D.H., Tumer, K., “Collective Intelligence for Optimization”, in “Statistical Machine Learning for Large-Scale Optimization”, J. Boyan, et al. (Ed.’s),

*Neural Computing Surveys*, 2000.

Wolpert, D.H., Kirshner, S., Tumer, K., Merz, C., “Adaptivity in Agent-Based Routing for Data Networks”, in

*Proceedings of Agents 00*, Sierra, C., et al, (Ed.s), 2000.

Wolpert, D.H., Wheeler, K., Tumer, K., “Collective Intelligence for Control of Distributed Dynamical Systems”,

*Europhysics Letters*, vol. 49 issue 6, 708-714, 2000.

Wolpert, D.H., Wheeler, K., Tumer, K., “General Principles of Learning-based Multi-Agent Systems”,

*Third International Conference of Autonomous Agents*, J.E. Bradshaw (Ed.), ACM Press, 77-83, 1999.

Wolpert, D.H., Tumer, K., Frank, J. “Using collective intelligence to route internet traffic”,

*Neural Information Processing Systems 11*, Kearns et al. (Eds), MIT Press, 952-958, 1999.

**Statistical Inference and Machine Learning**

Wolpert, D.H., and DeDeo, S., “Estimating Functions of Distributions Defined over Spaces of Unknown Size”, invited contribution to

*Entropy*, in press.

Wolpert, D.H., “Supervised Learning Theory”, invited contribution to

*Encyclopedia of Cognitive Science*, Robert French et al. (Ed.’s), Macmillian Press, in press.

Wolpert, D.H. “The Supervised Learning No-Free-Lunch Theorems”, invited contribution to World conference on Soft Computing 2001, 2001.

Smyth, P. and Wolpert, D. H., “Linearly Combining Density Estimators via Stacking”,

*Machine Learning Journal*,

**36**, 59-83, 1999.

Wolpert, D.H., and Macready, W.G., “An Efficient Method to Estimate Bagging’s Generalization Error”,

*Machine Learning Journal*,

**35**, 41-55, 1999.

Smyth, P. and Wolpert, D. H., “Stacked Density Estimation”,

*Neural Information Processing Systems 10*, MIT Press, 1998.

Wolpert, D.H., Knill, E., and Grossman, T., “Some results concerning off-training-set and IID error for the Gibbs and Bayes optimal generalizers”,

*Statistics and Computing*,

**8**(1), March 1998, pp. 35--54.

Delwart, E.L., Pan, H., Sheppard, H.W., Wolpert, D.H.,Neumann, A.U., Korber, B.T., Mullins, J.I., “Slower Evolution of HIV-1 quasispecies evolution during progression to AIDS”,

*J. Virol*, October,

**71**(10), 7498-7508, 1997.

Smyth, P. and Wolpert, D. H., “Anytime Exploratory Data Analysis for Massive Data Sets”,

*The Third International Conference on Knowledge Discovery and Data Mining*, AAAI Press, 1997.

Wolpert, D.H., “On Bias plus Variance”,

*Neural Computation*,

**9**, 1997.

Wolpert, D.H., “The Lack of A Priori Distinctions between Learning Algorithms”,

*Neural Computation*,

**8**, 1341 - 1390, 1996.

Wolpert, D.H., “The Existence of A Priori Distinctions between Learning Algorithms”,

*Neural Computation*,

**8**, 1996.

Wolpert, D.H., “Determining Whether Two Data Sets are from the Same Distribution”, in

*Maximum Entropy and Bayesian Methods*

*1995*, Ed. K. Hanson and R. Silver, Kluwer Academic press, 1996.

Wolpert, D., Macready, W., “Combining Stacking with Bagging to Improve a Learning Algorithm”. Santa Fe Institute Technical Report 96-03-123, 1996.

Wolpert, D.H., “The Bootstrap is Inconsistent with Probability Theory”, in

*Maximum Entropy and Bayesian Methods 1995*, Ed. K. Hanson and R. Silver, Kluwer Academic press, 1996.

Wolpert, D.H., Strauss, C.E., “What Bayes has to say about the evidence procedure”, in

*Maximum Entropy and Bayesian Methods*

*1993*, Ed. G. Heidbreder, Kluwer Academic press, 1996.

Wolpert, D.H., “Reconciling Bayesian and non-Bayesian analysis”, in

*Maximum Entropy and Bayesian Methods*

*1993*, Ed. G. Heidbreder, Kluwer Academic press, 1996.

Kohavi, R., and Wolpert, D.H., “Bias Plus Variance Decomposition for Zero-One Loss Functions”,

*Proceedings of the International Machine Learning Conference 13*, Ed. Lorenza and Saiita, Morgan Kauffman,1996.

Wolpert, D.H., and Wolf, D.R., “Estimating Functions of Probability Distributions from a Finite Set of Samples”,

*Physical Review E*,

**52**, p. 6841, 1995. (Note subsequent erratum:

*Physical Review E*,

**54**, p. 6973, 1996.)

Wolpert, D.H., “Horizontal Generalization”, in

*Proceedings of the International Machine Learning Conference 12*, Ed. A. Prieditis and S. Russell, Morgan Kauffman, 1995.

Wolpert, D.H., “On the Bayesian 'Occam Factors' Argument for Occam's Razor”, in

*Computational Learning Theory and Natural Learning Systems*

*III*, Ed. T. Petsche et al., MIT Press, 1995.

Wolpert, D.H., “The Relationship Between the Various Supervised Learning Formalisms”, in

*The Mathematics of Generalization*, Ed. D. Wolpert, Addison-Wesley, 1994.

Wolpert, D.H., and Lapedes, A.S., “A Rigorous Investigation of Exhaustive Learning”, in

*The Mathematics of Generalization*, Ed. D. Wolpert, Addison-Wesley, 1994.

Wolpert, D.H., “Filter Likelihoods and Exhaustive Learning”, in

*Computational Learning Theory and Natural Learning Systems*

*II*, Ed. S. Hanson et al., MIT Press,1994.

Wolpert, D.H., “Bayesian back-propagation over I-O functions rather than weights”, in

*Advances in Neural Information Processing Systems VI*, Ed. S. Hanson et al., Morgan Kauffman, 1994.

Strauss, C.E., Wolpert, D.H., Wolf, D.R., “Alpha, Evidence, and the Entropic Prior”, in

*Maximum Entropy and Bayesian Methods 1992*, Ed. A. Mohammed-Djafari, Kluwer, 1994.

Wolpert, D.H., “Combining Generalizers Using Partitions of the Learning Set”, in

*1992 Lectures in Complex Systems*, Ed. L. Nadel et al., Addison-Wesley, 1994.

Wolpert, D.H., “On the Use of Evidence in Neural Networks”, in

*Advances in Neural Information Processing Systems V*, Ed. S. Hanson et al., Morgan Kauffman, 1993.

Korber, B.T., Farber, R.M., Wolpert, D.H., and Lapedes, A.S., “Covariation of Mutations in the V3 Loop of HIV-1: An Information-Theoretic Analysis”,

*Proceedings of the National Academy of Sciences*,

**90**, 7176-7180, 1993.

Wolpert, D.H., “How to Deal with Multiple Possible Generalizers”, in

*Fast Learning and Invariant Object Recognition*, Ed. B. Soucek, Wiley and Sons, 1992.

Wolpert, D.H., “Stacked Generalization”,

*Neural Networks*,

**5**, 241-259, 1992.

*This work was the basis of both winning entries in the 2009 netflix competition. See*

*J. Sill, G, Takacs, L. Mackey, and D. Lin,*

*“Feature-Weighted Linear Stacking” for details*

Wolpert, D.H., “On the Connection Between In-Sample Testing and Generalization Error”,

*Complex Systems*,

**6**, 47-94, 1992.

Wolpert, D.H., “The Relationship Between Occam's Razor and Convergent Guessing”,

*Complex Systems*,

**4**, 319-368, 1990.

Wolpert, D.H., “Using a Mathematical Theory of Generalization to Construct a Generalizer Superior to NETtalk”,

*Neural Networks*,

**3**, 445-452, 1990.

Wolpert, D.H., “A mathematical Theory of Generalization: part I, part II”,

*Complex Systems*,

**4**,151-200, 201-249, 1990.

Wolpert, D.H., “A benchmark for how well neural nets generalize”,

*Biological Cybernetics*,

**61**303-313, 1989.

**Physics and Computation**

Wolpert, D.H., “Information Width: a way for the second law to increase complexity”, in

*The Self-Organizing Universe: Cosmology, Biology, and the Rise of Complexity*, C. Lineweaver, P. Davies, and M. Ruse (Ed.’s), Cambridge University Press, in press.

Wolpert, D.H. and Benford, G., “The Lesson of Newcomb’s Paradox”,

*Synthese*, 2011.

Wolpert, D.H., “Inference concerning physical systems”, Proc. of CiE 2010, Fernando Ferreira, Benedikt Lowe, Elvira Mayordomo, Luis Mendes Gomes (Eds.), Springer, 2010

Wolpert, D.H., “Physical limits of inference”, Physica D,

**237**(2008) 1257-1281.

*See also Binder, P., “Theories of almost everything”, Nature,*

**455**(2008), 884-885Wolpert, D.H., “Computational Capabilities of Physical Systems”, Physical Review E, Vol. 65, 016128, Dec. 20, 2001.

Wolpert, D.H., “The Second Law, Computation, and the Temporal (A)symmetry of Memory”, in

*Advances in the Physics of Computation*, Ed. D. Matzke, IEEE press, 1993.

Wolpert, D.H. “Memory Systems, Computation, and The Second Law of Thermodynamics”,

*International Journal of Theoretical Physics*,

**31**, 743-785, 1992.

Wolpert, D.H., “Reversible Computing and Physical Law”,

*PHYSICS TODAY*, 98-99 (March 1992).

Wolpert, D.H., “Chaos of the Brussels School is not irreversible”,

*Nature*,

**335**, 595, 1988.

**Optimization and Search**

Wolpert, D. H. and Rajnarayan, D. “Using machine learning to improve Stochastic Optimization”,

*Proceedings AAAI 2013*, in press (2013).

Tracey, B. Wolpert, D.H., and Alonso, J.J., “Using Supervised Learning to Improve Monte Carlo Integral Estimation”,

*AIAA Journal*, in press.

Wolpert, D.H., “What the no free lunch theorems really mean; how to improve search algorithms

*Ubiquity Symposium*

*, ACM*, ubiquity.acm.org/symposia.cfm, 2012.

Tracey, B. Wolpert, D.H., and Alonso, J.J., “Using Supervised Learning to Improve Monte Carlo Integral Estimation”,

*13th AIAA Non-Deterministic Approaches Conference, Denver, CO, April 2011*, AIAA Paper 2011-1843

*.*

Wolpert, D. H., Rajnarayan, D., and Bieniawski S., “Probability Collectives in Optimization”,

*Encyclopedia of Stastistics*, C.R. Rao and V. Govindaraju (Ed.’s), in press.

Rajnarayan, D., and Wolpert, D. H. "Bias-Variance trade-offs: Novel Applications”,

*Encyclopedia of Machine Learning*, Claude Sammut, Geoffrey I. Webb (Ed.’s), Springer, 2011.

Rajnarayan, D. and Wolpert, D.H., “Bias-Variance Techniques for Monte Carlo Optimization: Cross-validation for the CE Method”, arXiv:0810.0877v1, 2008.

Rajnarayan, D. and Wolpert, D.H., “Exploiting Parametric Learning to Improve Black-Box Optimization”,

*Proc. ECCS 2007*, J. Jost et al. (Ed.)

Rajnarayan, D., Wolpert, D.H., Kroo, I. “Optimization Under Uncertainty Using Probability Collectives”,

*Proc. 11 AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference*, Portsmouth, VA, AIAA-2006-7033, 2006.

Wolpert, D.H., and Lee, C.F., “An adaptive Metropolis-Hastings scheme: sampling and optimization”,

*Europhysics Letters*,

**76**, 353-359, 2006.

Wolpert, D.H., and Macready, W.G., “Coevolutionary Free Lunches”,

*IEEE Transactions on Evolutionary Computation*,

**9**, 721-735, 2005.

Koeppen, M., Wolpert, D. H., Macready, W. G., “Remarks on a Recent Paper on the ‘No Free Lunch’ Theorems”, IEEE

*Transactions on Evolutionary Computation*,

**5**, pp. 295-296, June 2001.

Macready, W.G., and Wolpert, D.H., “Bandit Problems and the Exploration/Exploitation Tradeoff”,

*IEEE Transactions on Evolutionary Computation*,

**2**, 2-22, 1998.

Wolpert, D.H., and Macready, W.G., “No Free Lunch Theorems for Optimization”,

*IEEE Transactions on Evolutionary Computation*,

**1**, 1997.

Macready, W.G., and Wolpert, D.H. “What Makes an Optimization Problem Hard?”,

*Complexity*,

**5**, 1996.

**Other topics**

Wolpert, D.H., Macready, W., “Using Self-dissimilarity to Quantify Complexity”, Complexity,

**12**, 2007.

Wolpert, D.H., Macready, W., “Self-dissimilarity as a high dimensional complexity measure”, in International Conference on Complex Systems 2004, Y. Bar-Yam (Ed.), Perseus books, in press.

Wolpert, D.H., “Metrics for more than two points at once”, in International Conference on Complex Systems 2004, Y. Bar-Yam (Ed.), Perseus books, in press.

Wolpert, D.H., MacLennan, B. J., “A Computationally Universal Field Computer with Linear Dynamics”, Neural Computation, in press.

Wolpert, D.H., and Macready, W.G., “Self-Dissimilarity: An Empirically Observable Measure of Complexity”,

*Unifying Themes in Complex Systems”*, Y. Bar-Yam (Ed.), Perseus books, 2000.

Wolpert, D.H., and Maclennan, B., “A Computationally Universal Field Computer that is Purely Linear”, in

*Proceedings of the 5th Joint Conference on Information Sciences*”,

*(Atlantic City, NJ, Feb. 27 - Mar. 3, 2000),*

**I**, pp. 782-5, Paul P. Wang (Ed.), ACM Press, 2000.

**BOOKS**

Tumer, K. and Wolpert, D.H. (Ed.),

*Collectives And The Design Of Complex Systems*, Springer, 2004.

Wolpert, D.H. (Ed.),

*The Mathematics of Generalization*, Addison-Wesley, 1994.

Guy, T., Karny, N., Wolpert and D.H., (Ed.),

*Proceedings of NIPS 2011 workshop on “Decision Making and Imperfection”,*Springer, 2013.

Guy, T., Karny, N., Wolpert and D.H., (Ed.) ,

*Proceedings of NIPS 2010 workshop on “Decision Making with Multiple Imperfect Decision Makers”,*Springer, 2012.

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