Assortative Matching, Reputation, and the Beatles Break-Up

Consider Becker's (1973) classic static matching model, with output a stochastic function of unobserved types. Assume symmetric incomplete information about types, and thus commonly observed Bayesian posteriors. Matching is then assortative in these `reputations' if expected output is supermodular in types. We instead consider a standard dynamic version of this world, and discover a robust failure of Becker's global result. We show that as the production outcomes grow, assortative matching is neither efficient nor an equilibrium for high enough discount factors. Specifically, assortative matching fails around the highest reputation agents for `low-skill concealing' technologies. Our theory implies the dynamic result that high-skill matches (like the Beatles) eventually break~up. Our results owe especially to two findings: (a) value convexity due to learning undermines match supermodularity; and (b) for a fixed policy in optimal learning, the second derivative of the value function explodes geometrically at extremes.supermodularity, convexity

Caller Number Five and Related Timing Games

There are two varieties of timing games in economics: Having more predecessors helps in a war of attrition and hurts in a pre-emption game. This paper introduces and explores a spanning class with rank-order payoffs} that subsumes both as special cases. We assume a continuous time setting with unobserved actions and complete information, and explore how equilibria of these games capture many economic and social timing phenomena --- shifting between phases of slow and explosive (positive probability) stopping. Inspired by auction theory, we first show how the symmetric Nash equilibria are each equivalent to a different "potential function". This device straightforwardly yields existence and characterization results. The Descartes Rule of Signs, e.g., bounds the number phase transitions. We describe how adjacent timing game phases interact: War of attrition phases are not played out as long as they would be in isolation, but instead are cut short by pre-emptive atoms. We bound the number of equilibria, and compute the payoff and duration of each equilibrium.Games of Timing, War of Attrition, Preemption Game.

Assortative Matching and Reputation

Consider Becker's classic 1963 matching model, with unobserved fixed types and stochastic publicly observed output. If types are complementary, then matching is assortative in the known Bayesian posteriors (the 'reputations'). We discover a robust failure of Becker's result in the simplest dynamic two type version of this world. Assortative matching is generally neither efficient nor an equilibrium for high discount factors. In a labor theoretic rationale, we show that assortative matching fails around the highest (lowest) reputation agents for 'low-skill (high-skill) concealing' technologies. We then find that as the number of production outcomes grows, almost all technologies are of either form. Our theory implies the dynamic result that high-skill matches eventually break up. It also reveals that the induced information rents create discontinuities in the wage profile. This in turn produces life-cycle effects: young workers are paid less than their static marginal product, and old workers more.assortative matching, incomplete information, wages, Bayesian posterior, value function

Simultaneous Search

We introduce and solve a new class of "downward-recursive" static portfolio choice problems. An individual simultaneously chooses among ranked stochastic options, and each choice is costly. In the motivational application, just one may be exercised from those that succeed. This often emerges in practice, such as when a student applies to many colleges. We show that a greedy algorithm finds the optimal set. The optimal choices are "less aggressive" than the sequentially optimal ones, but "more aggressive" than the best singletons. The optimal set in general contains gaps. We provide a comparative static on the chosen set.college application, submodular optimization, greedy algorithm, directed search

Aspirational Bargaining

This paper offers a noncooperative behaviourally-founded solution of the complete information bargaining problem where two impatient individuals wish to divide a unit pie. We formulate the game in continuous time, with unrestricted timing and content of offers. Reprising experimental work from 1960, we introduce and explore aspirational equilibrium -- a Markovian refinement of subgame perfection where behaviour is governed by aspiration values (expected payoffs). The analysis is tractable, and generates many intuitive aspects of bargaining absent from the standard temporal monopoly paradigm: wars of attrition explains delay; serious offers are concessions; offers may be turned down, strictly disappointing the proposers, or accepted, strictly helping the proposer. In particular, an endogenous `proposee' advantage arises, as opposed to the hard-wired proposer standard advantage. We find that discounted aspiration values form a martingale, and thereby compute bounds on the expected bargaining duration from observed offers. We also deduce some simple implications about consecutive offers, and relate delay times, offers, and acceptance rates. Finally, we draw into question a traditional comparative static: Ceteris paribus, more impatient players can expect more of the pie.subgame perfect equilibrium, aspiration, extensive form

Informational Herding and Optimal Experimentation

We show that far from capturing a formally new phenomenon, informational herding is really a special case of single-person experimentation - and `bad herds' the typical failure of complete learning. We then analyze the analogous team equilibrium, where individuals maximize the present discounted welfare of posterity. To do so, we generalize Gittins indices to our non-bandit learning problem, and thereby characterize when contrarian behaviour arises: (i) While herds are still constrained efficient, they arise for a strictly smaller belief set. (ii) A log-concave log-likelihood ratio density robustly ensures that individuals should lean more against their myopic preference for an action the more popular it becomes.herding; optimal learning; experimentation; contrarianism

Informational Herding and Optimal Experimentation

We show that far from capturing a formally new phenomenon, informational herding is really a special case of single-person experimentation -- and 'bad herds' the typical failure of complete learning. We then analyze the analogous team equilibrium, where individuals maximize the present discounted welfare of posterity. To do so, we generalize Gittins indices to our non-bandit learning problem, and thereby characterize when contrarian behaviour arises: (i) While herds are still constrained efficient, they arise for a strictly smaller belief set. (ii) A log-concave log-likelihood ratio density robustly ensures that individuals should lean more against their myopic preference for an action the more popular it becomes.Bayesian learning, value function, herding, experimentation, log concavity, Gittins index, team equilibrium

The Demand for Information: More Heat than Light

This paper produces a comprehensive theory of the value of Bayesian information and its static demand. Our key insight is to assume 'natural units' corresponding to the sample size of conditionally i.i.d. signals -- focusing on the smooth nearby model of the precision of an observation of a Brownian motion with uncertain drift. In a two state world, this produces the heat equation from physics, and leads to a tractable theory. We derive explicit formulas that harmonize the known small and large sample properties of information, and reveal some fundamental properties of demand: (a) Value 'non-concavity': The marginal value of information is initially zero; (b) The marginal value is convex/rising, concave/peaking, then convex/falling; (c) 'Lumpiness': As prices rise, demand suddenly chokes off (drops to 0); (d) The minimum information costs on average exceed 2.5% of the payoff stakes; (e) Information demand is hill-shaped in beliefs, highest when most uncertain; (f) Information demand is initially elastic at interior beliefs; (g) Demand elasticity is globally falling in price, and approaches 0 as prices vanish; and (h) The marginal value vanishes exponentially fast in price, yielding log demand. Our results are exact for the Brownian case, and approximately true for weak discrete informative signals. We prove this with a new Bayesian approximation result.Value of information, Non-concavity, Heat equation, Demand, Bayesian analysis

The College Admissions Problem Under Uncertainty

We consider a college admissions problem with uncertainty. We realistically assume that (i) students' college application choices are nontrivial because applications are costly, (ii) college rankings of students are noisy and thus uncertain at the time of application, and (iii) matching between colleges and students takes place in a decentralized setting. We analyze a general equilibrium model where two ranked colleges set admissions standards for student quality signals, and students, knowing their types, decide where to apply to. We show that the optimal student application portfolio need not be monotone in types, and we construct a robust example to show that this can lead to a failure of assortative matching in equilibrium. More importantly, we prove that a unique equilibrium with assortive matching exists provided application costs are small and the lower-ranked college has sufficiently high capacity. We also provide equilibrium comparative static results with respect to college capacities and application costs. We apply the model to the question of race-based admissions policiesmatching, directed search, noise

We Can't Argue Forever

We analyze time-costly decision-making in committees by privately informed individuals, such as juries, panels, boards, etc. In the spirit of the Coase Conjecture, we show that the decision is "almost instantaneous" when individuals entertain identical objectives. Delay can only be understood as the outcome of conflicting (biased) objectives.