1. Download Github Copilot. Add Python module. 2. Get a Claude Console subscription. Select Claude Sonnet 3.7 in Github Copilot. 3. Open the Chat window and select Agent. 4. Enter “Can you write an ABM of U.S. trade that considers the deficit, debt, trade imbalances, and international capital flows. Watch project be populated. 5. Press Run. 6. Play with dog.
From: Friam <friam-boun...@redfish.com> on behalf of Pieter Steenekamp <piet...@randcontrols.co.za> Date: Saturday, April 5, 2025 at 3:45 AM To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com> Subject: Re: [FRIAM] Fwd: CSSSA April Webinar I listened to the above webinar on Agent-Based Modeling (ABM) in Economics and Finance, and would like to share a few reflections: It would be wonderful to see this discipline develop further. In fields like transportation planning, ABM has already matured to a point where it arguably outperforms traditional top-down approaches. A few years ago in South Africa, ABM was used in planning a major public transport upgrade in Gauteng. I followed the project closely and, in my view, it was a great success. My friend Johan Joubert led the modeling effort, and the results were impressive. But let me return to ABM in the context of Economics and Finance. I understand that building effective ABM models in these domains is significantly more challenging than in transportation. Yet, imagine the value if it becomes a reality. The U.S., for example, is grappling with major economic issues: a growing federal deficit, mounting government debt, a persistent trade imbalance, and a population—especially the lower half—feeling economically left behind. Wouldn’t it be exciting if ABM could contribute to practical, data-driven solutions to these kinds of complex problems? I was a bit disappointed that the webinar didn’t mention the potential integration of ABM with AI models in the context of Economics and Finance. There’s so much potential here. Large language models (LLMs) could help generate more nuanced and adaptable ABM scenarios, while ABM could provide rich, dynamic environments to train and refine AI models—especially reinforcement learning systems aimed at supporting policy-making. I’m optimistic that this kind of synergy will emerge in the near future. On Sat, 29 Mar 2025 at 09:53, Stephen Guerin <stephen.gue...@simtable.com <mailto:stephen.gue...@simtable.com>> wrote: ---------- Forwarded message --------- From: Computational Social Science Society of the Americas <newslet...@computationalsocialscience.org <mailto:newslet...@computationalsocialscience.org>> Date: Fri, Mar 28, 2025, 7:10 PM Subject: CSSSA April Webinar To: <stephen.gue...@simtable.com <mailto:stephen.gue...@simtable.com>> View this email in your browser <https://mailchi.mp/6007323ec35a/csssa-april-webinar?e=8cb4039763> Dear CSSSA members, We are very excited to host Robert Axtell and Doyne Farmer discussing “Agent-Based Modeling in the Economics and Finence” in our 2025 webinar series on Wednesday, April 2nd, at 10 am (ET) . Click here to register for the webinar <https://computationalsocialscience.us9.list-manage.com/track/click?u=4f667f8d01c7a2b9a0490726c&id=2172fd08e1&e=8cb4039763> Abstract In a long paper in the Journal of Economic Literature Axtell and Farmer review agent-based modeling (ABM) in economics and finance and highlight how it can be used to relax conventional assumptions in standard models. ABM has enriched the understanding of markets, industrial organization, labor, macro, development, and environmental economics. In finance, substantial accomplishments include understanding clustered volatility, market impact, systemic risk, and housing markets. A vision is presented for how ABMs might be used in the future to build more realistic models of the economy. Hurdles that must be overcome to achieve this are discussed. Their paper includes more than 800 references including many from adjacent fields. Biographs Professor Axtell is the author, with Joshua Epstein, of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press). His research has appeared in Science, Nature, Proceedings of the National Academy of Sciences, as well as in leading field-specific journals such as The Journal of Economic Literature, The American Economic Review, The Economic Journal, and many others. His research has been reprised in newspapers (e.g., Wall St. Journal, Los Angeles Times, Washington Post) and science magazines (e.g., Scientific American, Technology Review, Wired). For the past decade he has been using microdata on individuals to build large-scale models of the Financial Crisis of 2008-9 (with JD Farmer, Oxford, and J Geanakoplos, Yale), the dynamics of business firms (with O Guerrero, Turing Institute), and natural resource exploitation, e.g., fisheries (with UC Santa Barbara, Oxford, and the Ocean Conservancy). The research on companies is described at length in a forthcoming book, ‘Dynamics of Firms from the Bottom Up: Data, Theories, and Models’, due out next year, which uses U.S. micro-data on firm sizes, ages, growth rates, networks, and locations to create a model at 1:1 scale with the American economy. Prof. Doyne Farmer is an American complex systems scientist and entrepreneur with interests in chaos theory, complexity and econophysics. He has published papers in Science and Nature as well as leading economics journals like the Journal of Economic Behavior & Organization. He is Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, Oxford University, where he is also director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School. Additionally, he is an external professor at the Santa Fe Institute. His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. He has recently published a book entitled ‘Making Sense of Chaos: A Better Economics for a Better World.’ CSSSA Secretary is inviting you to a scheduled Zoom meeting. 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