Economics

The broad concept of an individual's welfare is actually a cluster of related specific concepts that bear a "family resemblance" to one another. One might care about how a policy will affect people both in terms of their subjective preferences and also in terms of some notion of their objective interests. This paper provides a framework for evaluation of policies in terms of welfare criteria that combine these two considerations. Sufficient conditions are provided for such a criterion to imply the same ranking of social states as does Pareto's unanimity criterion. Sufficiency is proved via study of a community of agents with interdependent ordinal preferences.

This article shows a new focus of mathematic analysis for the Solow-Swan economic growth model, using the generalized conformal derivative Katugampola (KGCD). For this, under the same Solow-Swan model assumptions, the Inada conditions are extended, which, for the new model shown here, depending on the order of the KGCD. This order plays an important role in the speed of convergence of the closed solutions obtained with this derivative for capital (k) and for per-capita production (y) in the cases without migration and with negative migration. Our approach to the model with the KGCD adds a new parameter to the Solow-Swan model, the order of the KGCD and not a new state variable. In addition, we propose several possible economic interpretations for that parameter.

We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions. Persuasion may totally collapse in a Markov perfect equilibrium (MPE) of this game. However, for persuasion costs sufficiently small, a version of a folk theorem holds: outcomes that approximate Kamenica and Gentzkow (2011)'s sender-optimal persuasion as well as full revelation and everything in between are obtained in MPE, as the cost vanishes.

For a many-to-many matching market, we study the lattice structure of the set of random stable matchings. We define a partial order on the random stable set and present two intuitive binary operations to compute the least upper bound and the greatest lower bound for each side of the matching market. Then, we prove that with these binary operations the set of random stable matchings forms two dual lattices.

Large-scale institutional changes require strong commitment and involvement of all stakeholders. We use the standard framework of cooperative game theory developed by Ichiishi (1983, pp. 78-149) to: (i) establish analytically the difference between policy maker and political leader; (ii) formally study interactions between a policy maker and his followers; (iii) examine the role of leadership in the implementation of structural reforms. We show that a policy maker can be both partisan and non-partisan, while a political leader can only be non-partisan. Following this distinction, we derive the probability of success of an institutional change, as well as the nature of the gain that such a change would generate on the beneficiary population. Based on the restrictions of this simple mathematical model and using some evidence from the Congolese experience between 2012 and 2016, we show that institutional changes can indeed benefit the majority of the population, when policy makers are truly partisan.

Consider a set of agents who play a network game repeatedly. Agents may not know the network. They may even be unaware that they are interacting with other agents in a network. Possibly, they just understand that their payoffs depend on an unknown state that is, actually, an aggregate of the actions of their neighbors. Each time, every agent chooses an action that maximizes her instantaneous subjective expected payoff and then updates her beliefs according to what she observes. In particular, we assume that each agent only observes her realized payoff. A steady state of the resulting dynamic is a selfconfirming equilibrium given the assumed feedback. We characterize the structure of the set of selfconfirming equilibria in the given class of network games, we relate selfconfirming and Nash equilibria, and we analyze simple conjectural best-reply paths whose limit points are selfconfirming equilibria.

We study a dynamic stopping game between a principal and an agent. The agent is privately informed about his type. The principal learns about the agent's type from a noisy performance measure, which can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/market crashes are often preceded by a spike in (expected) performance. Our model also predicts that, due to endogenous signal manipulation, too much transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.

Agents learn about a changing state using private signals and past actions of neighbors in a network. We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses.

Savage (1972) lays down the foundation of Bayesian decision theory, but asserts that it is not applicable in big worlds where the environment is complex. Using the theory of finite automaton to model belief formation, this paper studies the characteristics of optimal learning behavior in small and big worlds, where the complexity of the environment is low and high, respectively, relative to the cognitive ability of the decision maker. Confirming Savage's claim, optimal learning behavior is closed to Bayesian in small worlds but significantly different in big worlds. In addition, I show that in big worlds, the optimal learning behavior could exhibit a wide range of well-documented non-Bayesian learning behavior, including the use of heuristic, correlation neglect, persistent over-confidence, inattentive learning, and other behaviors of model simplification or misspecification. These results establish a clear and testable relationship between the prominence of non-Bayesian learning behavior, complexity and cognitive ability.

Vicarious learning is a vital component of organizational learning. We theorize and model two fundamental processes underlying vicarious learning: observation of actions (learning what they do) vs. belief sharing (learning what they think). The analysis of our model points to three key insights. First, vicarious learning through either process is beneficial even when no agent in a system of vicarious learners begins with a knowledge advantage. Second, vicarious learning through belief sharing is not universally better than mutual observation of actions and outcomes. Specifically, enabling mutual observability of actions and outcomes is superior to sharing of beliefs when the task environment features few alternatives with large differences in their value and there are no time pressures. Third, symmetry in vicarious learning in fact adversely affects belief sharing but improves observational learning. All three results are shown to be the consequence of how vicarious learning affects self-confirming biased beliefs.