Innovators, Imitators, and the Evolving Architecture of Social Networks

Scientific progress is driven by innovation — which serves to produce a diversity of ideas — and imitation through a social network — which serves to diffuse these ideas. In this paper, we develop an agent-based computational model of this process, in which the agents in the population are heterogeneous in their abilities to innovate and imitate. The model incorporates three primary forces — the discovery of new ideas by those with superior abilities to innovate, the observation and adoption of these ideas by those with superior abilities to communicate and imitate, and the endogenous development of social networks among heterogeneous agents. The objective is to explore the evolving architecture of social networks and the critical roles that the innovators and imitators play in the process. A central finding is that the emergent social network takes a chainstructure with the innovators as the main source of ideas and the imitators as the connectors between the innovators and the masses. The impact of agent heterogeneity and environmental volatility on the network architecture is also characterized.

The Impact of the Corporate Leniency Program on Cartel Formation and the Cartel Price Path

Previous research exploring the effect of corporate leniency programs has modelled the oligopoly stage game as a Prisoners’ Dilemma. Using numerical analysis, we consider the Bertrand price game and allow the probability of detection and penalties to be sensitive to firms’ prices. Consistent with earlier results, a maximal leniency program necessarily makes collusion more difficult. However, we also find that partial leniency programs - such as in the U.S. - can make collusion easier compared to offering no leniency. We also show that even if cartel formation is not deterred, a leniency program can reduce the prices charged by firms.

Optimal Corporate Leniency Programs

This study characterizes the corporate leniency policy that minimizes the frequency with which collusion occurs. Though it can be optimal to provide only partial leniency, plausible sufficient conditions are provided whereby the antitrust authority should waive all penalties for the first firm to come forward. It is also shown that restrictions should be placed on when amnesty is awarded, though it can be optimal to award amnesty even when the antitrust authority is very likely to win the case without insider testimony.

Detecting Cartels

In reviewing the theoretical and empirical literature on collusion, this paper distills methods for detecting cartels and distinguishing collusion from competition.

Estimating a Semi-Parametric Duration Model without Specifying Heterogeneity

This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and time-varying regressors. In particular, this paper allows for discrete measurement of the durations as happens often in practice. The integrated baseline hazard and all parameters are estimated at regular rate,square root of N , where N is the number of individuals. A hazard model is a natural framework for time-varying regressors if a flow or a transition probability depends on a regressor that changes with time since a hazard model avoids the curse of dimensionality that would arise from interacting the regressors at each point in time with one another.

Foundations of Bayesian Theory

This paper states necessary and sufficient conditions for the existence, uniqueness, and updating according to Bayes’ rule, of subjective probabilities representing individuals’ beliefs. The approach is preference based, and the result is an axiomatic subjective expected utility model of Bayesian decision making under uncertainty with statedependent preferences. The theory provides foundations for the existence of prior probabilities representing decision makers’ beliefs about the likely realization of events and for the updating of these probabilities according to Bayes’ rule.

Subjective Expected Utility Theory without States of the World

This paper develops an axiomatic theory of decision making under uncertainty that dispenses with the state space. The results are subjective expected utility models with unique, action-dependent, subjective probabilities, and a utility function defined over wealth-effect pairs that is unique up to positive linear transformation.

Do World Shocks Drive Domestic Business Cycles? Some Evidence from Structural Estimation

Existing results on the contribution of terms of trade and world interest rate shocks to output fluctuations in small open economies range from less than 10% to almost 90%. We argue that an dentification problems lies at the heart of these vastly different results. In this paper, we overcome this by estimating a DSGE model using a structural Bayesian estimation approach. We apply our methodology to five developed and developing economies.. Our approach allows us to efficiently exploit cross-equation restrictions implied by the structural model. We find that world interest rate shocks are the main driving forces of business cycles in small open economies while terms of trade shocks are not.

A Bayesian Look at New Open Economy Macroeconomics

This paper develops a small-scale two country model following the New Open Economy Macroeconomics paradigm. Under autarky the model specializes to the familiar three equation New Keynesian dynamic stochastic general equilibrium (DSGE) model. We discuss two challenges to successful estimation of DSGE models: potential model misspecification and identification problems. We argue that prior distributions and Bayesian estimation techniques are useful to cope with these challenges. We apply these techniques to the two-country model and fit it to data from the U.S. and the Euro Area. We compare parameter estimates from closed and open economy specifications, study the sensitivity of parameter estimates to the choice of prior distribution, examine the propagation of monetary policy shocks, and assess the model's ability to explain exchange rate movements.

The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems

This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided.

A New Approach to Modeling Decision-Making under Uncertainty and Defining Subjective Probabilities

This paper presents axiomatic models of decision making under uncertainty that avoid the use of a state space. The models are (a) general subjective expected utility theory with action-dependent subjective probabilities and effect-dependent utilities (the cases of effect-independent preferences and effect-independent valuations are obtained as special instances) and (b) a nonexpected utility theory involving well-defined families of action-dependent subjective probabilities on effects and utility representation that is not necessarily linear in these probabilities (a probabilistic sophistication version of this model, with action-dependent subjective probabilities is obtained as a special case).