Learning backward induction: a neural network agent approach

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. In particular a very simple heuristic socialmax, that chooses the cell with the highest social payoff explains their behavior approximately 60% of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two heuristics were found to be ecologically valid for the backward induction problem as they predicted the Nash equilibrium in 67% and 50% of the games respectively. Compared to various standard classification algorithms, the NNs were found to be only slightly more accurate than standard discriminant analyses. However, the latter do not model the dynamic learning process and have an ad hoc postulated functional form. In contrast, a NN agent’s behavior evolves with experience and is capable of taking on any functional form according to the universal approximation theorem.

Technology Adoption in Poorly Specified Environments

This article extends the characteristics-based choice framework of technology adoption to account for decisions taken by boundedly-rational individuals in environments where traits are not fully observed. It is applied to an agricultural setting and introduces the concept of ambiguity in the agricultural technology adoption literature by relaxing strict informational and cognition related assumptions that are implied by traditional Bayesian analysis. The main results confirm that ambiguity increases as local conditions become less homogeneous and as computational ability, own experience and nearby adoption rates decrease. Measurement biases associated with full rationality assumptions are found to increase when decision makers have low computational ability, low experience and when their farming conditions differ widely from average adopter ones. A complementary empirical paper (Useche 2006) finds that models assuming low confidence in observed data, ambiguity and pessimistic expectations about traits predict sample shares better than models which assume that farmers do not face ambiguity or are optimistic about the traits of new varieties.Research and Development/Tech Change/Emerging Technologies,

Rationally Inattentive Savers and Monetary Policy Changes: A Laboratory Experiment

We study the response of consumption and saving decisions of rationally inattentive individuals to changes in monetary policy in the laboratory. First, we theoretically characterize the choices of a rationally inattentive agent processing information about the interest rate. Then, we design an experiment with induced inattention to test for the predictions of the model, contrasting them to the full information case. Consistent with the predictions, experimental subjects (a) increase attention when utility gains exceed cognitive costs of tracking the policy rate and decrease savings when their perceived economic outlook deteriorates; (b) respond to Delphic, but not Odyssean, forms of forward guidance. These findings agree with recent empirical evidence on monetary policy effects on consumption behavior in U.S. and internationally

Behavioural Anomalies, Bounded Rationality and Simple Heuristics

The use of bounded rationality in explaining economic phenomena has attracted growing attention. In spite of this, there is still considerable disagreement regarding the meaning of bounded rationality. Basov (2005) argues that when modeling boundedly rational behaviour it is desirable to start with an explicit formulation of the learning process. A complete understanding of the boundedly rational decision-making process requires development of an evolutionary-dynamic model which can give rise to such learning processes. Evolutionary dynamics implies that individuals use heuristics to adjust their choices in light of past experiences, moving in the direction that appears most beneficial, where these adjustment rules are assumed ‘hardwired’ into human cognition through the process of biological evolution. In this paper we elaborate on the latter point by building a model of evolutionary selection relevant to heuristics. We show that in addition to explaining the origin of learning rules this approach also sheds light on some well documented preference anomalies.Bounded Rationality;Heuristics;Replicator Dynamics