Constrained Data-Fitters,
with Samuelson, presentation, plenary talk
We study maximum-likelihood estimation and updating,
subject to computational, cognitive, or behavioral
constraints. We jointly characterize
constrained estimates and updating within a framework reminiscent of a machine
learning algorithm. Without frictions,
the framework simplifies to standard maximum-likelihood estimation and Bayesian
updating. Our central finding is that
under certain intuitive cognitive constraints, simple models yield the most
effective constrained fit to data---more complex models offer a superior fit,
but the agent may lack the capability to
assess this fit accurately. With some
additional structure, the agent's problem is isomorphic to a familiar rational
inattention problem.
Growth and
Redistribution: The Hedging Perspective, with Samuelson, conditionally accepted to AER:insights, longer
version
We investigate the impact of wealth redistribution on
economic growth, building on Kelly's (1956) optimal investment portfolio
theory. A growth-optimal policy redistributes wealth from 'lucky'
overperforming individuals to underperforming individuals, minimizing the
systematic component of this redistribution in a myopic fashion. That is, the
policy minimizes the discrepancy between endowments and outcomes,
counterfactually taking outcomes as independent of endowments. Alternatively,
we reinterpret this result in terms of maximum likelihood estimation of a
distribution over both latent and observable variables. Beliefs derived from
the estimated joint distribution fail Bayes' plausibility due to
misspecification; however, the estimate myopically minimizes this failure.
Risk Perception: Measurement
and Aggregation, accepted to JEEA,
with Netzer,
Robson and Kocourek, presentation
In a model inspired by neuroscience, we study choice
between lotteries as a process of encoding and decoding noisy perceptual
signals. The implications of this process for behavior
depend on the decision-maker's understanding of risk. When the aggregation of
perceptual signals is coarse, encoding and decoding generate behavioral risk attitudes even for vanishing perceptual
noise. We show that the optimal encoding of lottery rewards is S-shaped and
that low-probability events are optimally oversampled. Taken together, the
model can explain adaptive risk attitudes and probability weighting, as in
prospect theory. Furthermore, it predicts that risk attitudes are influenced by
the anticipation of risk, time pressure, experience, salience, and availability
heuristics.
Boundedly Rational Demand, accepted to Theoretical Economics with Kocourek and Stewart,
Evidence suggests that consumers do not perfectly
optimize, contrary to a critical assumption of classical consumer theory. We
propose a model in which consumer types can vary in both their preferences and
their choice behavior. Given data on demand and the
distribution of prices, we identify the set of possible values of the consumer
surplus based on minimal rationality conditions: every type of consumer must be
no worse off than if they either always bought the good or never did. We
develop a procedure to narrow the set of surplus values using richer datasets
and provide bounds on counterfactual demands.
Decision Theory and Stochastic
Growth, with Robson
and Samuelson, American Economic Review: insights 5(3),
2023, 357-76 presentation, přednáška, homework
assignment
This paper examines connections between stochastic
growth and decision problems. We use
tools from the theory of large deviations to show that wishful thinking
decision problems are equivalent to utility maximization problems, both of
which are equivalent to growth maximization under idiosyncratic risk. Rational inattention problems are equivalent
to growth-optimal portfolio problems, both of which are equivalent to growth
maximization under aggregate risk. Stochastic growth generates extreme
inequality, with nearly all wealth eventually held by those who happen to have
faced empirical distributions that match the solution to the wishful thinking
or rational inattention problem.
Attention
Please! with Gossner and Stewart, 2021, Econometrica 89(4), 1717-1751,
presentation, puzzle
We study the impact of manipulating the attention of a
decision-maker who learns sequentially about a number of items before making a
choice. Under natural assumptions on the decision-maker's strategy, directing
attention toward one item increases its likelihood of being chosen regardless
of its value. This result applies when the decision-maker can reject all items
in favor of an outside option with known value; if no
outside option is available, the direction of the effect of manipulation
depends on the value of the item. A similar result applies to manipulation of
choices in bandit problems.
Optimal Test Allocation,
2021, with Ely, Galeotti and Jann, J. Econ. Theory 105236
Rotation as Contagion
Mitigation, 2021, with Ely and Galeotti, Management Science, 67(5), 3117-3126
(ideas from this paper have been incorporated into the DELVE
report of the Royal Society)
Habits as Adaptations: An
Experimental Study, 2020, with Matyskova,
Rogers, and Sun, presentation, Game
Econ Behav 122, 391-406
Selective
Sampling with Information-Storage Constraints, 2020, with Jehiel, presentation, Economic Journal, 1753-1781
On the Cost of
Misperception: General Results and Behavioral
Applications, 2018, with Gossner, J. Econ. Theory 177, 816-847, presentation
Rational Inattention Dynamics:
inertia and delay in decision-making, 2017, with Stewart
and Matějka, Econometrica 85(2), 521-553,
presentation, popularizační přednáška
Perceiving Prospects Properly,
2016, with Stewart,
American Economic Review 106,
1601-31, presentation, press
summary by AEA
Price Distortions under Coarse
Reasoning with Frequent Trade, 2015, with Stewart, J.
Econ. Theory 159, 574-595, presentation, supplement
Influential Opinion Leaders,
2014, with Stewart
and Loeper, Economic
Journal 124, 1147-1167
Tractable Dynamic
Global Games and Applications, 2013, with Mathevet, J.
Econ. Theory 148, 2583-2619
Reversibility in Dynamic
Coordination Problems, 2013, with Kováč, Game Econ Behav 77, 298-320
Who Matters in Coordination Problems?,
2012, with Sákovics,
American Economic Review 102(7),
3439-3461
Dynamic
Coordination with Private Learning, 2012, with Dasgupta and Stewart, Game Econ Behav 74, 83-101
Communication, Timing, and Common Learning,
2011, with Stewart, J.
Econ. Theory 146, 230-247,
(the paper explained on the blog
of Jeff Ely)
Contagion through Learning, 2008, with Stewart, Theoretical
Economics 3, 431-458
Coordination of Mobile Labor, 2008, J.
Econ. Theory 139(1), 25-46
Coordination Cycles, 2008, Game Econ Behav 63(1), 308-327
The Effects of Risk Aversion in Mixed-Strategy
Equilibria of 2x2 Games, 2007, with Engelmann, Game Econ Behav 60, 381-388
A
Trace of Anger is Enough: On the Enforcement of Social Norms, 2007, Economics
Bulletin, vol. 8.
Dynamic
scaling and universality in evolution of fluctuating random networks, 2002,
with Kotrla and Slanina, Europhys. Lett. 60, 14-20