2025 Theses Doctoral

Essays in Behavioral and Experimental Economics

Panico de Lara, Lucas

In this dissertation, I revisit two classic phenomena in behavioral economics: choice overload and the gambler's fallacy. Despite their claims to fame, our understanding of these two phenomena are insufficient, especially in comparison to the attention that they have received. All chapters in this dissertation bring two methodologies to remedy this situation, namely microeconomic theory and laboratory experiments. Competing theoretical explanations for each of the two phenomena are formally presented and analyzed, and an experimental design is formulated in order to produce data that is capable of differentiating between such competing explanations.

Chapter 1 presents and experimentally tests a collection of search theoretic explanations for `choice overload', the phenomena by which a default alternative is selected more often in larger choice sets. A standard search model, with constant search costs and a known distribution of item quality, cannot give rise to choice overload. If one instead assumes that either (i) the Decision Maker (DM) must learn the quality distribution (ii) search costs are increasing or (iii) the DM decides the search strategy in advance, then choice overload can occur. Unlike existing models, our approach does not require ad hoc psychological costs (decision avoidance), or for the DM to assume the choice set was selected by a profit maximizing firm (contextual inference). Data from our laboratory experiments are consistent with choice overload caused by search with learning and increasing costs, and cannot be explained by decision avoidance or contextual inference. A classic explanation for the gambler's fallacy is that subjects believe that sequences of tosses from a fair coin should be representative of the randomness of the uniform distribution, and so should not have observable patterns.

In chapter 2, I introduce an information-theoretic formalization of this representativeness heuristic in terms of complexity and contrast it to the existing recency-weighted reversal model by Rabin and Vayanos. In order to test between these explanations, I collect rich choice and belief data from subjects predicting the next item from fully randomized sequences of binary outcomes, allowing me to take the analysis to the level of individual sequences. The basic results confirm the existence of the gambler’s fallacy in the aggregate. However, there is also significant heterogeneity among subjects. I identify four types, depending on whether they report correct beliefs or incorrect beliefs that go in the gambler's fallacy direction, its opposite or a mix of both. Taking this heterogeneity into account, both models perform well when looking at an aggregate level, but a closer look at individual sequences reveals violations of the representativeness model which lead to a superior performance of the recency-weighted reversal model. The main component of this superior performance comes from the recency bias that subjects exhibit, which is not accounted for in the representativeness model.

Chapter 3 continues the work in chapter 2 by extending the framework for predictions about the next two outcomes instead of just one. The models that were presented in chapter 2 are extended to this new scenario and a novel experiment is presented, in which multidimensional belief and choice data is collected. The results from this experiment further reinforce those from chapter 2. Particularly striking is the finding that subjects suffering from the classic definition of gambler's fallacy believe, in the case of alternating sequences, the continuation of the alternating pattern to be the most likely outcome.