Optimizing systematic technology adoption with heterogeneous agents

Chen, H. & Ma, T. (2017). Optimizing systematic technology adoption with heterogeneous agents. European Journal of Operational Research 257 (1) 287-296. 10.1016/j.ejor.2016.07.007.

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The traditional operational optimization models of systematic technology adoption commonly assume the existence of a global social planner and ignore the existence of heterogeneous decision makers who interact with each other. This paper develops a stylized (or conceptual) optimization model of systematic technology adoption with heterogeneous agents (i.e., decision makers) and uncertain technological learning. Each agent attempts to identify optimal solutions to adopting technologies for a portion of the entire system. The agents in the model have different foresight and different risk attitudes and interact with one another in terms of technological spillover.

This paper first illustrates that although a well recalibrated representative model can perform well enough when the interest is placed on aggregate variables, it could react to a policy (a carbon tax in this paper) differently from the multi-agent model. Then this paper explores how the agents’ heterogeneities and interactions affect the optimal solutions of systematic technology adoption. The main findings of the study are that (1) the existence of multiple agents implies a slower adoption of advanced technologies in the entire system than assuming the existence of a global social planner, (2) with homogeneous agents, technological spillover tends to enhance the lock-in effect on previous technologies, and (3) with heterogeneous agents, even a small technological spillover rate can significantly accelerate the adoption of the advanced technology.

Item Type: Article
Uncontrolled Keywords: OR in societal problem analysis; Systematic technology adoption; Optimization; Heterogeneous agents; Uncertain technological learning
Research Programs: Transitions to New Technologies (TNT)
Depositing User: Luke Kirwan
Date Deposited: 22 Nov 2016 14:43
Last Modified: 27 Aug 2021 17:41
URI: https://pure.iiasa.ac.at/13984

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