Oblivious Equilibrium Research Articles

A Structural Empirical Model of R&D, Firm Heterogeneity, and Industry Evolution*

This article develops and estimates an industry equilibrium model of the Korean electric motor industry from 1991 to 1996. Plant‐level decisions on R&D, physical capital investment, entry, and exit are integrated in a dynamic setting with knowledge spillovers. We apply the novel approximation of oblivious equilibrium to estimate the R&D cost, magnitude of knowledge spillovers, adjustment costs of physical investment, and plant scrap value distribution. Knowledge spillovers are essential to explaining the firm‐level productivity evolution and the equilibrium market configuration. A R&D subsidy maximizes industry output and is broadly consistent with a past policy initiative of the Korean government.

Dynamic Games in Empirical Industrial Organization

This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional choice probabilities. We also discuss extensions of the basic framework, including models in continuous time, the concepts of oblivious equilibrium and experience-based equilibrium, and dynamic games where firms have non-equilibrium beliefs. In section 3, we first provide an overview of the types of data used in this literature, before turning to a discussion of identification issues and results, and estimation methods. We review different methods to deal with multiple equilibria and large state spaces. We also describe recent developments for estimating games in continuous time and incorporating serially correlated unobservables, and discuss the use of machine learning methods to solving and estimating dynamic games. Section 4 discusses empirical applications of dynamic games in IO. We start describing the first empirical applications in this literature during the early 2000s. Then, we review recent applications dealing with innovation, antitrust and mergers, dynamic pricing, regulation, product repositioning, advertising, uncertainty and investment, airline network competition, dynamic matching, and natural resources. We conclude with our view of the progress made in this literature and the remaining challenges.

Dynamic Games in Empirical Industrial Organization

This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional choice probabilities. We also discuss extensions of the basic framework, including models in continuous time, the concepts of oblivious equilibrium and experience-based equilibrium, and dynamic games where firms have non-equilibrium beliefs. In section 3, we first provide an overview of the types of data used in this literature, before turning to a discussion of identification issues and results, and estimation methods. We review different methods to deal with multiple equilibria and large state spaces. We also describe recent developments for estimating games in continuous time and incorporating serially correlated unobservables, and discuss the use of machine learning methods to solving and estimating dynamic games. Section 4 discusses empirical applications of dynamic games in IO. We start describing the first empirical applications in this literature during the early 2000s. Then, we review recent applications dealing with innovation, antitrust and mergers, dynamic pricing, regulation, product repositioning, advertising, uncertainty and investment, airline network competition, dynamic matching, and natural resources. We conclude with our view of the progress made in this literature and the remaining challenges.

Information Elicitation Mechanisms for Statistical Estimation

We study learning statistical properties from strategic agents with private information. In this problem, agents must be incentivized to truthfully reveal their information even when it cannot be directly verified. Moreover, the information reported by the agents must be aggregated into a statistical estimate. We study two fundamental statistical properties: estimating the mean of an unknown Gaussian, and linear regression with Gaussian error. The information of each agent is one point in a Euclidean space.Our main results are two mechanisms for each of these problems which optimally aggregate the information of agents in the truth-telling equilibrium:• A minimal (non-revelation) mechanism for large populations — agents only need to report one value, but that value need not be their point.• A mechanism for small populations that is non-minimal — agents need to answer more than one question.These mechanisms are “informed truthful” mechanisms where reporting unaltered data (truth-telling) 1) forms a strict Bayesian Nash equilibrium and 2) has strictly higher welfare than any oblivious equilibrium where agents' strategies are independent of their private signals. We also show a minimal revelation mechanism (each agent only reports her signal) for a restricted setting and use an impossibility result to prove the necessity of this restriction.We build upon the peer prediction literature in the single-question setting; however, most previous work in this area focuses on discrete signals, whereas our setting is inherently continuous, and we further simplify the agents' reports.

Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective

This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed second-price auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.

Oblivious equilibrium for concentrated industries

This article explores the application of oblivious equilibrium (OE) to highly concentrated markets. We define a natural extended notion of OE, called partially oblivious equilibrium (POE), that allows for there to be a set of strategically important firms (the “dominant” firms), whose firm states are always monitored by every other firm in the market. We perform computational experiments that explore the characteristics of POE, OE, and Markov perfect equilibrium (MPE), and find that POE generally performs well in highly concentrated markets. We also derive error bounds for evaluating the performance of POE for cases where MPE cannot be computed.

The impact of advertising regulation on industry: the cigarette advertising ban of 1971

This article studies the impact of the 1971 TV/radio advertising ban on the cigarette industry. Data indicate that industry advertising spending decreased sharply immediately following the ban but recovered and actually exceeded the preban level within five years. A dynamic oligopoly model of advertising is developed to incorporate two potential explanations. The estimated model fully accounts for the puzzling trend, with 74% of the postban advertising spending increase explained by industry dynamics, and 26% explained by learning. Furthermore, this article uses the new concept of nonstationary oblivious equilibrium to handle intractable state space and accelerate equilibrium computation.

Industry dynamics: Foundations for models with an infinite number of firms

This paper explores the connection between three important threads of economic research offering different approaches to studying the dynamics of an industry with heterogeneous firms. Finite models of the form pioneered by Ericson and Pakes (1995) capture the dynamics of a finite number of heterogeneous firms as they compete in an industry, and are typically analyzed using the concept of Markov perfect equilibrium (MPE). Infinite models of the form pioneered by Hopenhayn (1992), on the other hand, consider an infinite number of infinitesimal firms, and are typically analyzed using the concept of stationary equilibrium (SE). A third approach uses oblivious equilibrium (OE), which maintains the simplifying benefits of an infinite model but within the more realistic setting of a finite model. The paper relates these three approaches. The main result of the paper provides conditions under which SE of infinite models approximate MPE of finite models arbitrarily well in asymptotically large markets. Our conditions require that the distribution of firm states in SE obeys a certain “light-tail” condition. In a second set of results, we show that the set of OE of a finite model approaches the set of SE of the infinite model in large markets under a similar light-tail condition.

Computational Methods for Oblivious Equilibrium

Oblivious equilibrium is a new solution concept for approximating Markov-perfect equilibrium in dynamic models of imperfect competition among heterogeneous firms. In this paper, we present algorithms for computing oblivious equilibrium and for bounding approximation error. We report results from computational case studies that serve to assess both efficiency of the algorithms and accuracy of oblivious equilibrium as an approximation to Markov-perfect equilibrium. We also extend the definition of oblivious equilibrium, originally proposed for models with only firm-specific idiosyncratic random shocks, and our algorithms to accommodate models with industry-wide aggregate shocks. Our results suggest that, by using oblivious equilibrium to approximate Markov-perfect equilibrium, it is possible to greatly increase the set of dynamic models of imperfect competition that can be analyzed computationally.

Computational Methods for Oblivious Equilibrium

Oblivious equilibrium is a new solution concept for approximating Markov perfect equilibrium in dynamic models of imperfect competition among heterogeneous firms and has recently been used in multiple economic studies. In this paper, we present algorithms for computing oblivious equilibrium and for bounding approximation error. We report results from computational case studies that serve to assess both efficiency of the algorithms and accuracy of oblivious equilibrium as an approximation to Markov perfect equilibrium. We also extend the definition of oblivious equilibrium, originally proposed for models with only firm-specific idiosyncratic random shocks, and our algorithms to accommodate models with industry-wide aggregate shocks. Our results suggest that, by using oblivious equilibrium to approximate Markov perfect equilibrium, it is possible to greatly increase the set of dynamic models of imperfect competition that can be analyzed computationally.

Markov Perfect Industry Dynamics With Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We develop a simple algorithm for computing an “oblivious equilibrium,” in which each firm is assumed to make decisions based only on its own state and knowledge of the long run average industry state, but where firms ignore current information about competitors’ states. We prove that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain “lighttail” condition, then oblivious equilibria closely approximate Markov perfect equilibria. We develop bounds that can be computed to assess the accuracy of the approximation for any given applied problem. Through computational experiments, we find that the method often generates useful approximations for industries with hundreds of firms and in some cases even tens of firms.

Markov Perfect Industry Dynamics with Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We develop a simple algorithm for computing an "oblivious equilibrium," in which each firm is assumed to make decisions based only on its own state and knowledge of the long run average industry state, but where firms ignore current information about competitors' states. We prove that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain "light-tail" condition, then oblivious equilibria closely approximate Markov perfect equilibria. We develop bounds that can be computed to assess the accuracy of the approximation for any given applied problem. Through computational experiments, we find that the method often generates useful approximations for industries with hundreds of firms and in some cases even tens of firms.