Centrality in a network is highly valuable. This paper investigates the idea that the timing of entry into the network is a crucial determinant of a node's final centrality. We propose a model of strategic network growth which makes novel predictions about the forward-looking behaviors of players. In particular, the model predicts that agents entering the network at specific times will "vie for dominance"; that is they will make more connections than is myopically optimal in hopes of receiving additional connections from future players and thereby becoming dominant. The occurrence of these opportunities varies non monotonically with the parameters of the game. In a laboratory experiment, we find that players do exhibit “vying for dominance” behavior, but do not always do so at the predicted critical times. We find that a model of heterogeneous risk aversion best fits the observed deviations from initial predictions. Timing determines whether players have the opportunity to become dominant, but individual characteristics determine whether players exploit that opportunity.

In models of rational inattention, information costs are usually modeled using mutual information, which measures the expected reduction in entropy between prior and posterior beliefs, or ad hoc functional forms, but little is known about what form these costs take in reality. We show that under mild assumptions on information cost functions, including continuity and convexity, gross payoffs to decision makers are non-decreasing and continuous in potential rewards. We conduct laboratory experiments consisting of simple perceptual tasks with fine-grained variation in the level of potential rewards that allow us to test several hypotheses about rational inattention and compare various models of information costs via information criteria. We find that most subjects exhibit monotonicity in performance with respect to potential rewards, and there is mixed evidence on continuity and convexity of costs. Moreover, a significant portion of subjects are likelier to make small mistakes than large ones, contrary to the predictions of mutual information. This suggests that while people are generally rationally inattentive, their cost functions may display non-convexities or discontinuities, or they may incorporate some notion of perceptual distance. The characteristics of a decision-maker’s information cost function have implications for various economic applications, including investment.

We use laboratory experiments to test models of ‘rational inattention,’ in which people acquire information to maximize utility from subsequent choices net of information costs. We show that subjects adjust their attention in response to changes in incentives a manner which is broadly in line with the rational inattention model but which violates models such as random utility in which attention is fixed. However, our results are not consistent with information costs based on Shannon entropy, as is often assumed in applied work. We find more support for a class of ‘posterior separable’ cost functions which generalize the Shannon model.

Economic models of network formation are notoriously complex, particularly in the presence of history dependence and strategic forethought. However, these complex environments can generate interesting and potentially important behavior which would not be possible in simpler environments. In this paper, we introduce a simple network formation model which can generate strategically interesting behavior. We observe "Vying for Dominance" where early players make many connections in order to receive free connections from future players and "Favor Stacking" where one connection can be used to strategically encourage many others based on sun spots and dynamic interdependence. The basic model is very diffcult to solve in large networks, but we create and analyze two simplified models to isolate these specifc strategic features and explore them further.

Previous economic models of memory have failed to incorporate one of its critical features: decay. It has long been established that memories fade over time, losing fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other economically interesting phenomena across a wide range of economic contexts. We apply the framework to models of elections, insurance purchasing, and advertising. In these settings, the framework reproduces empirically established behaviors and produces additional insights.

In this paper we describe and characterize an informational tool which is primarily useful for improving the performance of noisy memory systems: re-encoding. Re-encoding is the process of periodically attempting to restore damaged or decayed memories to a better state. This can help avoid the accumulation of errors which ultimately leads to total information loss. We also show that a Bayesian form of re-encoding in the human mind could be responsible for a number of features of human memory including constructed memory, the poor correlation between memory accuracy and confidence, rehearsal effects, and specific spacing effects. It can also explain the observed way that memories decay over time.

Abstract Public goods provision continues to be a major problem of interest for economics. Many current methods require government level intervention. In cases where governments are unable or unwilling to intervene, lower power mechanisms are required. Current best practice in this case is the provision point mechanism, typically used by Kickstarter, but this mechanism does not eliminate the potential for the free rider problem. We propose a novel Minimum-Offer Contribution Mechanism where each player makes an offer and then each contribution is equal to the lowest offer made. This mechanism eliminates the free rider problem and implements the Lindahl (1958) equilibrium in weakly dominant strategies.

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In recent years, a great deal of work has been done on the idea of rational attention, the concept that people can rationally choose the type of quality of signal they receive about the world with more informative signals being more costly. Thus far, however, most models of rational inattention have not been able to incorporate perceptual distance, ie the fact that some states of nature are easier to distinguish than others. In this paper, we will be proposing and testing a number of models of rational attention which incorporate perceptual distance. We consider models based on normal signals, Markov transition matrices, and several models in which the probability of confusing two states is a fixed function of their distance under some metric. The models are tested and compared using data from incentivized dot counting and angle differentiation tasks.

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