Instance-Based Decision Making Model of Repeated Binary Choice

We describe an instance-based model of decision-making for repeated binary choice. The model provides an accurate account of existing data of aggregate choice probabilities and individual differences, as well as newly collected data on learning and choice interdependency. In particular, the model provides a general emergent account of the risk aversion effect that does not require any metacognitive assumptions. Advantages of the model include its simplicity, its compatibility with previous models of choice and dynamic control, and the strong constraints it inherits from the underlying cognitive architecture.

[1]  G. Logan.  Toward an instance theory of automatization. , 1988 .

[2]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[3]  Z. Dienes,et al.  The role of specific instances in controlling a dynamic system , 1995 .

[4]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[5]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[6]  Scott Sanner,et al.  Achieving Efficient and Cognitively Plausible Learning in Backgammon , 2000, ICML.

[7]  Christian Lebiere,et al.  Simple games as dynamic, coupled systems: randomness and other emergent properties , 2001, Cognitive Systems Research.

[8]  J. Tenenbaum,et al.  Generalization, similarity, and Bayesian inference. , 2001, The Behavioral and brain sciences.

[9]  De Vries.  Book review: R.C. O'Reilly and Y. Munakata: Computational explorations in cognitive neuroscience: understanding the mind by stimulating the brain. Cambridge, Mass: The MIT Press. , 2002 .

[10]  Cleotilde Gonzalez,et al.  Instance-based learning in dynamic decision making , 2003 .

[11]  Cleotilde Gonzalez,et al.  Learning in Dynamic Decision Making: The Recognition Process , 2003, Comput. Math. Organ. Theory.

[12]  D. Wallach,et al.  Conscious and unconscious knowledge: Mapping to the symbolic and subsymbolic levels of a hybrid architecture , 2003 .

[13]  L. Jiménez.  Attention and implicit learning , 2003 .

[14]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[15]  Cleotilde Gonzalez.  Learning to Make Decisions in Dynamic Environments: ACT-R Plays the Beer Game , 2004, ICCM.

[16]  I. Erev,et al.  On adaptation, maximization, and reinforcement learning among cognitive strategies. , 2005, Psychological review.

[17]  Daniel John Zizzo,et al.  Transfer of knowledge in economic decision making , 2005 .

[18]  C. Lebiere,et al.  Instance-Based Cognitive Models of Decision-Making , 2005 .

[19]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[20]  R. O’Reilly,et al.  Computational Explorations in Cognitive Neuroscience , 2009 .