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24
Computational Rationalization: The Inverse Equilibrium Problem
"... Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the singleagent decisiontheoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision ..."
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Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the singleagent decisiontheoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can then be used to accurately predict or imitate future behavior in similar observed or unobserved situations. In this work, we consider similar tasks in competitive and cooperative multiagent domains. Here, unlike singleagent settings, a player cannot myopically maximize its reward — it must speculate on how the other agents may act to influence the game’s outcome. Employing the gametheoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior, as well as recovering a reward function in these domains. 1.
On the descriptive value of loss aversion in decisions under risk. Available at SSRN: http://ssrn.com/abstract=1012022 Ert
 Psychological Review
, 2010
"... Previous studies of loss aversion in decisions under risk have led to mixed results. Losses appear to loom larger than gains in some settings, but not in others. The current paper clarifies these results by highlighting six experimental manipulations that tend to increase the likelihood of the behav ..."
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Previous studies of loss aversion in decisions under risk have led to mixed results. Losses appear to loom larger than gains in some settings, but not in others. The current paper clarifies these results by highlighting six experimental manipulations that tend to increase the likelihood of the behavior predicted by loss aversion. These manipulations include: (1) framing of the safe alternative as the status quo; (2) ensuring that the choice pattern predicted by loss aversion maximizes the probability of positive (rather than zero or negative) outcomes; (3) the use of high nominal (numerical) payoffs; (4) the use of high stakes; (5) the inclusion of highly attractive risky prospects that creates a contrast effect; and (6) the use of long experiments in which no feedback is provided and in which the computation of the expected values is difficult. In addition, the results suggest the possibility of learning in the absence of feedback: The tendency to select simple strategies, like “maximize the worst outcome ” which implies “loss aversion”, increases when this behavior is not costly. Theoretical and practical implications are discussed.
A loser can be a winner: Comparison of two instancebased learning models in a market entry competition
 Games
, 2011
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Decision Theory with ResourceBounded Agents
, 2013
"... There have been two major lines of research aimed at capturing resourcebounded players in game theory. The first, initiated by Rubinstein [18], charges an agent for doing costly computation; the second, initiated by Neyman [13], does not charge for computation, but limits the computation that agent ..."
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There have been two major lines of research aimed at capturing resourcebounded players in game theory. The first, initiated by Rubinstein [18], charges an agent for doing costly computation; the second, initiated by Neyman [13], does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach,
I’m Doing as Well as I Can: Modeling People as Rational Finite Automata
"... We show that by modeling people as bounded finite automata, we can capture at a qualitative level the behavior observed in experiments. We consider a decision problem with incomplete information and a dynamically changing world, which can be viewed as an abstraction of many realworld settings. We p ..."
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We show that by modeling people as bounded finite automata, we can capture at a qualitative level the behavior observed in experiments. We consider a decision problem with incomplete information and a dynamically changing world, which can be viewed as an abstraction of many realworld settings. We provide a simple strategy for a finite automaton in this setting, and show that it does quite well, both through theoretical analysis and simulation. We show that, if the probability of nature changing state goes to 0 and the number of states in the automaton increases, then this strategy performs optimally (as well as if it were omniscient and knew when nature was making its state changes). Thus, although simple, the strategy is a sensible strategy for a resourcebounded agent to use. Moreover, at a qualitative level, the strategy does exactly what people have been observed to do in experiments. 1
1 Maximization, Learning and Economic Behavior
, 2014
"... The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economi ..."
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The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, while it is also true that actual human behavior falls systematically short of perfect rationality. The current analysis provides a possible explanation of this apparent inconsistency. It suggests that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that under these conditions people quickly learn to maximize expected return. The review also shows that there are many situations in which experience does not increase maximization. In many cases experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists inter
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, 2012
"... doi: 10.3389/fmicb.2012.00019 The genes and enzymes of phosphonate metabolism by ..."
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doi: 10.3389/fmicb.2012.00019 The genes and enzymes of phosphonate metabolism by
Article Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games
, 2011
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