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Doyne Farmer, and in general the field of complexity economics, is big on agent based economic modeling.

by dqpb 4y ago

rntn 4y ago
A very approachable opinion article co-authored by Farmer in 2009 for Nature is available in pdf here : https://www.researchgate.net/publication/51437577_The_Economy_Needs_Agent-Based_Modeling https://www.researchgate.net/publication/51437577_The_Econom... An interesting perspective on agents in human factor complex situations of scientific analysis as part of the overall progression of the scientific process since Galileo is here : https://europepmc.org/article/pmc/pmc8962940 https://europepmc.org/article/pmc/pmc8962940 Finally, a survey of the progress of research in the behavioral areas of social and economic research is here : https://link.springer.com/article/10.1007/s43546-021-00103-3 https://link.springer.com/article/10.1007/s43546-021-00103-3
huitzitziltzin 4y ago
What economists who practice it mean by “agent based economic modeling” is a nothingburger with no influence on the wider field of economics. Generally what we economists want is to solve an inverse problem: 1. Suppose that agents are behaving according to a utility function with a set of parameters, let’s estimate those parameters. (Utility functions are restricted in form by economic theory.) (One Example: an individual’s utility function may be characterized by a degree of risk aversion - given observed data on many individuals, can we recover their risk aversion? Another Example: a firm’s investment decisions may depend on (intrinsically unobservable) fixed costs - given observed data on firm behavior in some industry, can we recover fixed costs which are compatible with the model and the observed decisions?) 2. Given the parameters we estimated, what sort of interesting counterfactual policies can we study, under the assumption that the parameters we estimated above don’t change? This is why you see “less reinforcement learning than you may expect”. Sure, I can write down a model of a macroeconomic environment and put some reinforcement learning agents in it who may learn to behave optimally given the environment. Are the parameters governing those RL models relevant to individuals and their decisions? I have no idea. Compare the alternative (inverse) approach of writing down a plausible reinforcement learning model and figuring out what parameters govern agents’ decisions from observed decision data. It’s a very different problem. Source: me, an economist.

Doyne Farmer, and in general the field of complexity economics, is big on agent based economic modeling.

by dqpb 4y ago

Doyne Farmer, and in general the field of complexity economics, is big on agent based economic modeling.

rntn 4y ago

A very approachable opinion article co-authored by Farmer in 2009 for Nature is available in pdf here : https://www.researchgate.net/publication/51437577_The_Economy_Needs_Agent-Based_Modeling https://www.researchgate.net/publication/51437577_The_Econom... An interesting perspective on agents in human factor complex situations of scientific analysis as part of the overall progression of the scientific process since Galileo is here : https://europepmc.org/article/pmc/pmc8962940 https://europepmc.org/article/pmc/pmc8962940 Finally, a survey of the progress of research in the behavioral areas of social and economic research is here : https://link.springer.com/article/10.1007/s43546-021-00103-3 https://link.springer.com/article/10.1007/s43546-021-00103-3

huitzitziltzin 4y ago

What economists who practice it mean by “agent based economic modeling” is a nothingburger with no influence on the wider field of economics. Generally what we economists want is to solve an inverse problem: 1. Suppose that agents are behaving according to a utility function with a set of parameters, let’s estimate those parameters. (Utility functions are restricted in form by economic theory.) (One Example: an individual’s utility function may be characterized by a degree of risk aversion - given observed data on many individuals, can we recover their risk aversion? Another Example: a firm’s investment decisions may depend on (intrinsically unobservable) fixed costs - given observed data on firm behavior in some industry, can we recover fixed costs which are compatible with the model and the observed decisions?) 2. Given the parameters we estimated, what sort of interesting counterfactual policies can we study, under the assumption that the parameters we estimated above don’t change? This is why you see “less reinforcement learning than you may expect”. Sure, I can write down a model of a macroeconomic environment and put some reinforcement learning agents in it who may learn to behave optimally given the environment. Are the parameters governing those RL models relevant to individuals and their decisions? I have no idea. Compare the alternative (inverse) approach of writing down a plausible reinforcement learning model and figuring out what parameters govern agents’ decisions from observed decision data. It’s a very different problem. Source: me, an economist.