This paper experimentally investigates cooperative game theory from a normative perspective. Subjects designated as Decision Makers express their view on what is fair for others, by recommending a payoff allocation for three subjects (Recipients) whose substitutabilities and complementarities are captured by a characteristic function. We show that axioms and solution concepts from cooperative game theory provide valuable insights into the data. Axiomatic and regression analysis suggest that Decision Makers’ choices can be (noisily) described as a convex combination of the Shapley value and equal split solution. A mixture model analysis, examining the distribution of Just Deserts indices describing how far one goes in the direction of the Shapley value, reveals heterogeneity across characteristic functions. Aggregating opinions by averaging, however, shows that the societal view of what is fair remains remarkably consistent across problems.
Consumers purchase multiple types of goods and services, but may be able to examine only a limited number of markets for the best price. We propose a simple model which captures these features, conveying some new insights. A firm’s price can deflect or draw attention to its market, and consequently, limited attention introduces a new dimension of competition across markets. We fully characterize the resulting equilibrium, and show that the presence of partially attentive consumers improves consumer welfare as a whole. When consumers are less attentive, they are more likely to miss the best offer in each market; but the enhanced cross-market competition decreases average price paid, as leading firms try to stay under the consumers’ radar.
Theories of bounded rationality are typically characterized over an exhaustive data set. How does one tell whether observed choices are consistent with a theory if the data is incomplete? How can out-of-sample predictions be made? What can be identified about preferences? This paper aims to operationalize some leading bounded rationality theories when the available data is limited, as is the case in most practical settings. We also point out that the recent bounded rationality literature has overlooked a methodological pitfall that can lead to ‘false positives’ and ‘empty’ out-of-sample predictions when testing choice theories with limited data.