Based on a critique on econometric and DSGE models (in particular in the context of the financial crisis), Doyne Farmer presents his current research programme that aims at building an agent-based model of the financial and economic crisis. It models heterogeneous agents and from there simulates the economy, firstly for the housing market. The interview gives a short insight in the research programme.

Antoine Godin gives a bright, illustrated, introduction to agent-based, stock-flow-consistent modeling, with a clear focus on the agent-based aspect.

One method of economic modelling that has become increasingly popular in academia, government and the private sector is Agent Based Models, or ABM. These simulate the actions and interactions of thousands or even millions of people to try to understand the economy – for this reason ABM was once described to me as being “like Sim City without the graphics”. One advantage of ABM is that it is flexible, since you can choose how many agents there are (an agent just means some kind of 'economic decision maker' like a firm, consumer, worker or government); how they behave (do they use complicated or simple rules to make decisions?); as well as the environment they act in, then just run the simulation and see what happens as they interact over time.

This article considers the strengths of agent-based modelling and the ways that it can be used to help central banks understand the economy. These models provide a complement to more traditional economic modelling which has been criticised in the wake of the Great Recession.

The usual background and distinctions between complexity and neoclassical economics are presented Neoclassical economics deals with perfectly rational representative agents this creates states of equilibrium On the other hand complexity economics relaxes these assumptions to deal with responsive agents in an uncertain dynamic environment this creates states of disequilibrium More …

Complexity economics and institutional economics are complementary approaches to studying the economy. They can pool their methods and foundational theories to explain the mechanisms that underlie economies.

This text provides an easy to understand introduction to complexity economics for non-specialist audiences such as bachelor's students.

In this course you'll learn about the tools used by scientists to understand complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks.

Der Kurs richtet sich an Interessierte, die einen Überblick über gängige Modellierungsansätze in der Makroökonomik erhalten wollen. Dabei habe ich versucht, Ansätze aus unterschiedlichen Paradigmen zu berücksichtigen, notwendigerweise auf Kosten einer gewissen Tiefe bei den einzelnen Modellierungsframeworks. Der Kurs setzt ein gewisses mathematisches Grundverständnis voraus, sollte aber für fortgeschrittene Bachelor-Studierende der Ökonomik und Master-Studierende von interdisziplinären Studienprogrammen gut geeignet sein. Die Themenauswahl ist natürlich notwendigerweise selektiv. Der erste, größere Abschnitt diskutiert Modelle für wirtschaftlichen Wandel in der langen Frist (Wachstumsmodelle). Der zweite, kürzere Abschnitte behandelt dann Modelle, welche versuchen eine langfristige mit einer kurzfristigen Perspektive zu verbinden.

The Microeconomics of Complex Economies uses game theory, modeling approaches, formal techniques, and computer simulations to teach useful, accessible approaches to real modern economies.

When Santa Fe Institute Scientists first started working on economics more than thirty years ago, many of their insights, approaches, and tools were considered beyond heterodox. These once-disparaged approaches included network economics, agents of limited rationality, and institutional evolution—all topics that are now increasingly considered mainstream.