Agent-based computational economics and African modeling:perspectives and challenges

Godwin Nwaobi ()

MPRA Paper from University Library of Munich, Germany

Abstract: In recent years, the government, of African Countries has assumed major responsibilities for economic reforms and growth. In attempting to describe their economies, economists (policymakers) in many African Countries have applied certain models that are by now widely known: Linear programming models, input-output models, macro-econometric models, vector auto regression models and computable general equilibrium models. Unfortunately, economies are complicated systems encompassing micro behaviors, interaction patterns and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning and multiple equilibria possibility. This paper therefore argues for the adoption of alternative modeling (bottom-up culture-dish) approach known as AGENT-BASED Computational Economics (ACE), which is the computational study of African economies modeled as evolving systems of autonomous interacting agents. However, the software bottleneck (what rules to write for our agents) remains the primary challenge ahead.

Keywords: artificial intelligence; computational laboratory; complex networks; multi-agent systems; agent-base computational economics; social networks; macro-econometric model; linear programming; input-output; vector auto regression; ace models; var models; neural networks; gene networks; derivatives; financial contagion; Africa economies; aceges models; energy (search for similar items in EconPapers)
JEL-codes: B4 C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 D5 (search for similar items in EconPapers)
Date: 2011-12-14
New Economics Papers: this item is included in nep-afr and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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