課程大綱：

Over the past twenty years, the use of simulation of the bottom-up emergence of markets has been developed by economists, engineers, and computer scientists. Indeed, there has been a convergence of interest in the application of simulation in general, and agent-based simulation in particular, to issues of analyzing and designing markets, both existing and newly invented.

Simulation of social interactions focuses on the complex adaptive behavior that emerges in social systems. To better understand the behavior of such complex adaptive social systems, “artificial worlds” composed of interacting adaptive agents can be created and analyzed. Such models often exhibit properties that are strikingly similar to the actual social world, e.g., cooperation, social norms, and social stratification into different classes, and provide a unique window into understanding such phenomena. Using simulation methods, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis. There is much research to be done in this area—along with creating and understanding these types of complex systems, efforts need to be directed toward developing accessible versions of these models for the classroom.

The purpose of the course is to introduce research students to tools of simulation in the social sciences and some applications in economics and market design.

Goals : To acquaint students with simulation in the social sciences, in general, and agent-based simulation in particular; its strengths and weaknesses, and its appropriateness for particular kinds and areas of research.

“Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs.” (Bratley, Fox & Schrage 1987, ix). While this definition is useful, it does not suggest the diverse purposes to which simulation can be put. These purposes include: prediction, performance, training, entertainment, education, proof and discovery.

“Simulation is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid in intuition.”

“Simulation is a way of doing thought experiments. While the assumptions may be simple, the consequences may not be at all obvious. The large-scale effects of locally interacting agents are called emergent properties of the system. Emergent properties are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction.” (Axelrod, 2003)

This course is a series of five three-hour lectures on this topic. As well as lectures and discussions, the course will include demonstrations of available computer platforms suitable for agent-based applications; in-class computer experiments; student experience at simulations; and in-class presentation of student work.

參考書目：