The Internet and the American Economic Association A Set of Proposals

The Internet poses numerous short- and long-term opportunities and challenges for almost all the operations of the American Economic Association (AEA). This article examines these challenges and proposes various actions. In general terms, the AEA should first adapt its operations to maintain a constant level of service to its members in this changing environment. Second, the AEA charter calls for the “encouragement of perfect freedom of economic discussion, ” and the cost-reducing promise of the Internet and associated computer technologies brings us closer to this goal, but their full potential requires their adoption by the AEA. Finally, the AEA is uniquely positioned as a large, capable nonprofit to implement these new services

Next Steps in the Information Infrastructure in Economics

Abstract. This paper updates Goffe and Parks (1997), which examined the Internet infrastructure of the economics profession and advocated changes to it. Much that was predicted and advocated in that paper has come to pass, yet the fundamentals of economic discourse have changed little. Rather than just proposing revolutionary changes, this paper explores the adoption of the most successful Internet service for economists to date, “Research Papers in Economics ” (RePEc), to explore the reasons for its success. It currently has metadata on more than 492,000 items. The paper then proposes new technologies for the profession and the incentives that might aid their adoption

Version 3.2 of Fortran Code for Simulated Annealing (a global optimization algorithm also useful as a local optimizer for difficult problems)

This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol. 60, no. 1/2, Jan./Feb. 1994, pp. 65-99. Simulated annealing is a global optimization method that distinguishes between different local optima. Starting from an initial point, the algorithm takes a step and the function is evaluated. When minimizing a function, any downhill step is accepted and the process repeats from this new point. An uphill step may be accepted. Thus, it can escape from local optima. This uphill decision is made by the Metropolis criteria. As the optimization process proceeds, the length of the steps decline and the algorithm closes in on the global optimum. Since the algorithm makes very few assumptions regarding the function to be optimized, it is quite robust with respect to non-quadratic surfaces. The degree of robustness can be adjusted by the user. In fact, simulated annealing can be used as a local optimizer for difficult functions. Briefly, we found it generally superior to multiple restarts of conventional optimization routines for difficult optimization problems. This code contains an example function and is ready to run. It is also heavily documented with numerous usage hints.