Economics

Consumers in many markets are uncertain about firms' qualities and costs, so buy based on both the price and the quality inferred from it. Optimal pricing depends on consumer heterogeneity only when firms with higher quality have higher costs, regardless of whether costs and qualities are private or public. If better quality firms have lower costs, then good quality is sold cheaper than bad under private costs and qualities, but not under public. However, if higher quality is costlier, then price weakly increases in quality under both informational environments.

Faced with huge market potential and increasing competition in emerging industries, product manufacturers with key technologies tend to consider whether to implement a component open supply strategy. This study focuses on a pricing game induced by the component open supply strategy between a vertically integrated manufacturer (who produces key components and end products) and an exterior product manufacturer (who produces end products using purchased key components) with different customer perceived value and different cost structure. This study first establishes a three stage pricing game model and proposes demand functions by incorporating relative customer perceived value. Based on the demand functions, we obtain feasible regions of the exterior manufacturer's sourcing decision and the optimal price decision in each region. Then the effects of relative customer perceived value, cost structure, and market structure on price decisions and optimal profits of the vertically integrated manufacturer are demonstrated. Finally, as for the optimal component supply strategy, we present a generalized closed supply Pareto zone and establish supply strategy Pareto zones under several specific configurations.

We consider a model where agents differ in their `types' which determines their voluntary contribution towards a public good. We analyze what the equilibrium composition of groups are under centralized and centralized choice. We show that there exists a top-down sorting equilibrium i.e. an equilibrium where there exists a set of prices which leads to groups that can be ordered by level of types, with the first k types in the group with the highest price and so on. This exists both under decentralized and centralized choosing. We also analyze the model with endogenous group size and examine under what conditions is top-down sorting socially efficient. We illustrate when integration (i.e. mixing types so that each group's average type if the same) is socially better than top-down sorting. Finally, we show that top down sorting is efficient even when groups compete among themselves.

This paper proposes a theory of pricing premised upon the assumptions that customers dislike unfair prices---those marked up steeply over cost---and that firms take these concerns into account when setting prices. Since they do not observe firms' costs, customers must extract costs from prices. The theory assumes that customers infer less than rationally: when a price rises due to a cost increase, customers partially misattribute the higher price to a higher markup---which they find unfair. Firms anticipate this response and trim their price increases, which drives the passthrough of costs into prices below one: prices are somewhat rigid. Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated; firms mitigate this perceived unfairness by reducing their markups; in general equilibrium, employment rises. The theory also features a hybrid short-run Phillips curve, realistic impulse responses of output and employment to monetary and technology shocks, and an upward-sloping long-run Phillips curve.

We introduce a model of probabilistic verification in a mechanism design setting. The principal verifies the agent's claims with statistical tests. The agent's probability of passing each test depends on his type. In our framework, the revelation principle holds. We characterize whether each type has an associated test that best screens out all the other types. In that case, the testing technology can be represented in a tractable reduced form. In a quasilinear environment, we solve for the revenue-maximizing mechanism by introducing a new expression for the virtual value that encodes the effect of testing.

We consider a problem where the principal chooses a project from a set of available projects for the agent to work on. Each project provides some profit to the principal and some payoff to the agent and these profits and payoffs are the agent's private information. The principal has a belief over these values and his problem is to find an incentive compatible mechanism without using transfers that maximizes expected profit. Importantly, we assume partial verifiability so that the agent cannot report a project to be more profitable to the principal than it actually is. In this setup, we find a neat characterization of the set of incentive compatible mechanisms. Using this characterization, we find the optimal mechanism for the principal when there are two projects. Within a subclass of incentive compatible mechanisms, we show that a single cutoff mechanism is optimal and conjecture that it is the optimal incentive compatible mechanism.

We investigate the containment of epidemic spreading in networks from a normative point of view. We consider a susceptible/infected model in which agents can invest in order to reduce the contagiousness of network links. In this setting, we study the relationships between social efficiency, individual behaviours and network structure. First, we exhibit an upper bound on the Price of Anarchy and prove that the level of inefficiency can scale up to linearly with the number of agents. Second, we prove that policies of uniform reduction of interactions satisfy some optimality conditions in a vast range of networks. In setting where no central authority can enforce such stringent policies, we consider as a type of second-best policy the shift from a local to a global game by allowing agents to subsidise investments in contagiousness reduction in the global rather than in the local network. We then characterise the scope for Pareto improvement opened by such policies through a notion of Price of Autarky, measuring the ratio between social welfare at a global and a local equilibrium. Overall, our results show that individual behaviours can be extremely inefficient in the face of epidemic propagation but that policy can take advantage of the network structure to design efficient containment policies.

We introduce a novel and general model of dynamic n-player Blotto contests. The players have asymmetric resources, the battlefields are heterogenous, and contest success functions are general as well. We obtain one possibility and one impossibility result. When players maximize the expected value of the battles, the strategy profile in which players allocate their resources proportional to the sizes of the battles at every history---whether their resources are fixed from the beginning or can be subject to shocks in time---is a subgame perfect equilibrium. However, when players maximize the probability of winning, there is always a distribution of values over the battles such that proportional resource allocation cannot be supported as an equilibrium.

This paper proposes a careful separation between an entity's epistemic system and their decision system. Crucially, Bayesian counterfactuals are estimated by the epistemic system; not by the decision system. Based on this remark, I prove the existence of Newcomb-like problems for which an epistemic system necessarily expects the entity to make a counterfactually bad decision. I then address (a slight generalization of) Newcomb's paradox. I solve the specific case where the player believes that the predictor applies Bayes rule with a supset of all the data available to the player. I prove that the counterfactual optimality of the 1-Box strategy depends on the player's prior on the predictor's additional data. If these additional data are not expected to reduce sufficiently the predictor's uncertainty on the player's decision, then the player's epistemic system will counterfactually prefer to 2-Box. But if the predictor's data is believed to make them quasi-omniscient, then 1-Box will be counterfactually preferred. Implications of the analysis are then discussed. More generally, I argue that, to better understand or design an entity, it is useful to clearly separate the entity's epistemic, decision, but also data collection, reward and maintenance systems, whether the entity is human, algorithmic or institutional.

Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper we study the following information disclosure problem in two-sided markets: If a platform wants to maximize revenue, which sellers should the platform allow to participate, and how much of its available information about participating sellers' quality should the platform share with buyers? We study this information disclosure problem in the context of two distinct two-sided market models: one in which the platform chooses prices and the sellers choose quantities (similar to ride-sharing), and one in which the sellers choose prices (similar to e-commerce). Our main results provide conditions under which simple information structures commonly observed in practice, such as banning certain sellers from the platform while not distinguishing between participating sellers, maximize the platform's revenue. An important innovation in our analysis is to transform the platform's information disclosure problem into a constrained price discrimination problem. We leverage this transformation to obtain our structural results.