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One of the primary challenges to measuring the impact of antitrust or competition policy on collusion is that the cartel population is unobservable; we observe only the population of discovered cartels. To address this challenge, a model of cartel creation and dissolution is developed to endogenously derive the populations of cartels and discovered cartels. With this theory, one can infer the impact of competition policy on the population of cartels by measuring its impact on the population of discovered cartels. In particular, changes in the duration of discovered cartels can be informative in assessing whether a new policy is reducing the latent rate of cartels. Disciplines Business | Economics | Public Affairs, Public Policy and Public Administration CommentsAt the time of publication, author Joseph E. Harrington, Jr. was affiliated with the John Hopkins University. Currently, he is a faculty member in the Business, Economy and Public Policy Department of the Wharton School at the University of Pennsylvania.This journal article is available at ScholarlyCommons: http://repository.upenn.edu/bepp_papers/25 MODELING THE BIRTH AND DEATH OF CARTELS WITH AN APPLICATION TO EVALUATING COMPETITION POLICYJoseph E. Harrington, Jr. Johns Hopkins University Myong-Hun ChangCleveland State University Abstract One of the primary challenges to measuring the impact of antitrust or competition policy on collusion is that the cartel population is unobservable; we observe only the population of discovered cartels. To address this challenge, a model of cartel creation and dissolution is developed to endogenously derive the populations of cartels and discovered cartels. With this theory, one can infer the impact of competition policy on the population of cartels by measuring its impact on the population of discovered cartels. In particular, changes in the duration of discovered cartels can be informative in assessing whether a new policy is reducing the latent rate of cartels. (JEL: L13, L41)

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A computational model of a retail chain is developed in which store managers continually search for better practices. Search takes place over a rugged landscape defined over the space of store practices. The main objective of this research is to determine how the amount of discretion given to store managers as to how they run their stores influences the rate of innovation at the store level. We find that greater decentralization enhances firm performance when stores' markets are sufficiently different, the horizon is sufficiently long, and markets are sufficiently stable.organizational structure, decentralization, innovation, retail chain

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe explore the evolution of the structure and performance of a social network in a population of individuals who search for local optima in diverse and dynamic task environments. Individuals choose whether to innovate or imitate and, in the latter case, from whom to learn. The probabilities of these possible actions respond to an individual's past experiences using reinforcement learning. Among some of our more interesting findings is that a population's performance is not monotonically increasing in either the reliability of the communication network or the productivity of innovation.