# Job Market Papers

- Vying for Dominance in Dynamic Network Formation
Because networks play an important role in the behavior of many economic systems, the study of network formation games can provide important insights. Previous literature on network formation has largely avoided both history dependent, growing networks and forward looking strategic agents. As a result, dynamics do not play a major role in most traditional network formation models. In this paper, we present a new model of network formation in which the forward looking strategic behaviors of agents play a considerable role in determining the outcome networks. In particular, this model generates a behavior called “vying for dominance” in which players make a larger number of connections than is myopically beneficial in order to receive more connections from future players. This model provides a stepping stone between traditional economic models of network formation and the preferential attachment models used in other disciplines. Close

- An Experiment on Dynamic Network Formation
In Neligh (2017), we propose a model of strategic network growth which makes novel predictions about the forward looking behaviors of players. In particular, the model predicts “vying for dominance” behavior in which players make more connections than is myopically optimal in hopes of receiving additional connections from future players. In order to test the model's applicability to settings in which individuals are the primary decision makers, we conduct a laboratory experiment in which participants make small networks in accordance with the rules of the model. We find that players do exhibit “vying for dominance” behavior, but players also display large deviations from the predicted behaviors under certain conditions. After considering several behavioral models of player actions such as Level-K and QRE, we find that a model of heterogeneous risk aversion best fits the observed data. Close

# Associated Notes

- Supplementary Note to Network Growth with Strategic Agents and History Dependence
In Neligh (2017) we explore a game of dynamic network formation with forward-looking strategic agents. We find that SPEs of the game are not generally tractable for large networks, although the game is tractable in small networks and in simplified versions of the game. In this note, instead of looking for SPNE’s of the game, we look for NE in which players behave consistently across network states. We establish necessary and sufficient conditions for the existence of such NE’s and provide an algorithm to determine if the conditions hold. The algorithm can also provide sufficient conditions for the existence of certain classes of SPE’s as well as fully characterizing the set of SPE’s when the geometric discount factor is zero. Close

# Developed Working Papers

- "Experimental Tests of Rational Inattention" (with Mark Dean), Undergoing Initial Review,
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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. Close

- "Estimating Information Cost Functions in Models of Rational Inattention" (with Ambuj Dewan)
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. Close

# Working Papers

- Rational Attention with Perceptual Distances (with Ambuj Dewan)
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. Close

- Rational Memory Modeled as Information Storage on a Lossy Medium
Memories tend to worsen over time with the probability of correctly remembering an event or fact dropping off as a power law as time progresses. We know that the process of remembering can be directly affected by a number of conscious mechanisms, such as repeated exposure and recall, as well as unconscious mechanisms, such as interference and emotional state. So far, these features of memory have remained largely disparate, not considered in the same mathematical framework. In this paper, we propose a more unified optimizing memory model using information theoretic tools. Players encode memories in a series of symbols on a lossy medium. They are assumed to rationally choose the encoding/decoding mechanism, the number of symbols dedicated to a specific memory, the number and distribution of recall events, and the decay rate of those symbols. This model can account for many of the observed facts relating to memory as well as providing testable novel predictions regarding the performance of subjects in incentivized memory tasks. Close

- Propagation of Technological Advancement through Multiple Technology Levels
Most consumer technologies do not exist in isolation. Generally, new products depend on some underlying technology which advances along with those technologies it supports. In this paper, we model the development of products in a multi-level technology environment in which one class of products (the peripherals) relies on another class of product (the platforms) to operate. This model uses investor and consumer behavior to explain how technological progress propagates through dependent technologies and to explain the how change in the quality of the underlying technology impact the ratio of platforms to peripherals as well as the overall performance of the market. The model can be extended to deal with technology systems with an arbitrary depth with each layer relying on the one beneath it. Close

NATE NELIGH

Ph.D. Candidate

Department of Economics

1022 International Affairs Building

420 West 118th Street

New York City, NY 10027

Phone: (303) 319-6175

nln2110@columbia.edu