This agent-based stock market simulator, which was originally programmed in NetLogo and later moved to R, captures the behavior of the market in a statistical sense. Which is to say, it shows how multiple traders following logical strategies can add up to a whole lot of randomness and unpredictability. Also known as autoregressive conditional heteroscedasticity and/or generalized autoregressive conditional heteroscedasticity, if I remember my statistics class correctly. But the article does not go into that.
Tag Archives: agent based modeling
climate, economics, and agent based models
This journal article is mostly over my head, but I found the introduction interesting. It talks about the use of equilibrium models most common in economics compared to emerging research into agent based models.
Complexity and the Economics of Climate Change: a Survey and a Look Forward
Excerpt:
Mitigation and adaptation to climate change represent governance challenges of an unprecedented scale because of their long-term horizon, their global nature and the massive uncertainties they involve. Against this background, equilibrium models generally used in Integrated Assessment Models (IAM) represent the economy as a system with a unique equilibrium, climate policy as an additional constraint in the optimization problem of the social planner and consider the uncertainty of climate-related damages to be predictable enough to be factored out in the expected utility of a representative agent. There is growing concern in the literature that this picture might convey a false impression of control (seePindyck,2013; Stern, 2013, 2016; Weitzman, 2013; Revesz et al., 2014; Farmer et al., 2015, among manycontributions) and that IAMs might underestimate both the cost of climate change and the benefits resulting from the transition to a low carbon-emission economy (Stern, 2016).
Network and agent-based models have been increasingly advocated as alternatives t to handle out-of-equilibrium dynamics, tipping points and large transitions in socio-economic systems (see e.g Tesfatsion and Judd, 2006; Balbi and Giupponi, 2010; Kelly et al., 2013; Smajgl et al., 2011; Farmer et al., 2015; Stern, 2016; Mercure et al., 2016). These classes of models consider the real world as a complex evolving system, wherein the interaction of many heterogeneous agents possibly reacting across different spatial and temporal scales give rise to the emergence of aggregate properties that cannot be deduced by the simple aggregation of individual ones (Flake, 1988; Tesfatsion and Judd, 2006). The development of agent-based integrated assessment model can overcome the shortfall of equilibrium models and ease stakeholder participation and scenario exploration (Moss et al., 2001; Moss, 2002a). Indeed, the higher degree of realism of ABMs (Farmer and Foley, 2009; Farmer et al., 2015) allows to involve policy makers in the process of the development of the model employed for policy evaluation (Moss, 2002b).
NetLogo and soil science
Here’s an example of NetLogo (a free agent-based modeling platform) applied to a soil science application.
Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems’ behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations. Here, we present a streamlined, user-friendly and open version of the individual-based model INDISIM-SOM, which describes the mineralization of soil carbon and nitrogen. It was implemented in NetLogo, a widely used and easily accessible software platform especially designed for individual-based simulation models. Including powerful means to observe the model behavior and a standardized documentation, this increases INDISIM-SOM’s range of potential uses and users, and facilitates the exchange among soil scientists as well as between different modeling approaches.
more on NetLogo
Here is a video on Youtube with more information on the agent-based modeling system Netlogo. I am intrigued by the claim, even though it is such a cliche, that even a child can use it.
R and NetLogo
I had never heard of Netlogo, which is a programming language for simulating and teaching agent based models. Agent-based modeling is important because it might be the key to real quantitative simulation in economics and the social sciences. You can keep drilling down into the links in this post from R-bloggers until you either run out of time or find out everything you want to know about it.