Keywords
learning agents, heterogeneous expectations, climatic risks, modular modelling
Start Date
1-7-2004 12:00 AM
Abstract
When insurance firms, energy companies, governments, NGOs, and other agents strive to manageclimatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework forinvestigating this subject, we present the LAGOM model family. It is based on modules depicting learningsocial agents. For managing climate risks, our agents use second order probabilities and update them by meansof a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modulesand the aggregate outcomes of their actions are implemented using further modules. The software system isimplemented as a series of parallel processes using the CIAMn approach. It is possible to couple modulesirrespective of the language they are written in, the operating system under which they are run, and the physicallocation of the machine.
Agents, Bayes, and Climatic Risks – A Modular Modelling Approach
When insurance firms, energy companies, governments, NGOs, and other agents strive to manageclimatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework forinvestigating this subject, we present the LAGOM model family. It is based on modules depicting learningsocial agents. For managing climate risks, our agents use second order probabilities and update them by meansof a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modulesand the aggregate outcomes of their actions are implemented using further modules. The software system isimplemented as a series of parallel processes using the CIAMn approach. It is possible to couple modulesirrespective of the language they are written in, the operating system under which they are run, and the physicallocation of the machine.
