I'm very excited about how AI can help build a toy agent-based model (ABM) to explore some of the basic questions around macroeconomic choices and citizen happiness.
With AI, it's surprisingly easy! Here’s my simple plan of action: 1. Use AI to generate an initial specification for the model. 2. Take a very small first step—build a tiny, simple ABM as a subset of the full vision. 3. Gradually expand the model in small, manageable steps. 4. Be flexible—I'm happy to change the spec as I go. *Progress So Far* - I’ve created a first draft of the model specification (see below). - I’ve asked AI for a simple implementation of a small part of the model. So far, I haven’t been happy with the results, but I'm working on it. - I’m experimenting with different free code-generating AIs and Python ABM libraries. No promises—I don’t have a lot of time for this. As long as I enjoy it and it doesn’t interfere too much with the rest of my life, I’ll keep going. But I might stop anytime. I don’t plan to post frequent updates—I doubt people want to see every discarded version. But if I create something I’m proud of, I’ll gladly share it. If anyone wants to follow the details (including the failures!) or collaborate, feel free to contact me privately. ------------------------------ *Draft Specification (with generous help from AI)* *Project Title:* *Agent-Based Model of Macroeconomic Choices and Citizen Happiness* *1. Purpose* To simulate and explore the dynamic relationships between macroeconomic policies, international trade, cultural variables, and citizen happiness. Key questions include: - What macroeconomic choices improve happiness in different societies? - How do fiscal and monetary policies influence long-term well-being? - How does trade between unequal countries affect both sides? - How does citizen trust affect policy outcomes? *2. Core Framework* *Citizen Happiness* is the central outcome, modeled based on how well individuals’ needs are met—drawing on *Maslow’s hierarchy*, enriched with ideas from *Viktor Frankl*: 1. *Physiological* – food, housing, health 2. *Safety* – job security, public order, savings 3. *Belonging* – social inclusion, family cohesion 4. *Esteem* – purpose, recognition, quality of employment 5. *Self-Actualization* – freedom, creativity, meaning Needs are fulfilled via: - Economic participation (jobs, income) - Access to services (health, education, safety) - Cultural factors (justice, inclusion, dignity) - Governance (trust, fairness, opportunity) *3. Key Entities* *3.1 Countries (Macro Agents)* - *Government*: tax systems, social/infrastructure spending, trade/immigration policy, bond issuance - *Central Bank*: interest rates, inflation/employment targets, monetary policy tools - *Culture*: trust levels, value systems (e.g., collectivism vs individualism) - *Development Status*: GDP per capita, infrastructure, HDI, etc. *3.2 Citizens (Agents)* - Demographics: age, education, wealth, location - Psychology: trust, risk aversion, purpose - Employment: job quality, income, meaning - Needs satisfaction (Maslow levels) - Migration potential *3.3 Businesses* - Sector (tech, services, manufacturing, etc.) - Size, productivity - Interest rate sensitivity, government dependencies - Labor demand and wages - Job quality (innovation, autonomy) - Domestic vs multinational *4. Economic Mechanisms* *4.1 Government Policies* - Adjust tax/spending levels - Issue bonds - Regulate/encourage industries - Manage immigration policies *4.2 Central Bank Policies* - Set interest rates → affects savings, borrowing, inflation - Balance employment vs inflation goals *4.3 Global Trade* - Comparative advantage-based trade - Tariffs, subsidies, quotas - Exchange rates and trade balances - Debt transmission and policy contagion *5. Dynamics & Feedback Loops* - Citizen happiness → political pressure → policy change - Gov. spending → business environment → jobs → happiness - Trust → tax compliance → service levels → happiness - Export reliance → debt → austerity → happiness shifts - Inequality → unrest/trust erosion → instability *6. Output & Visualizations* - Happiness Index over time - Maslow-level satisfaction graphs - Social trust metrics - Inequality (e.g., Gini coefficient) - Employment, wages, job quality - Government fiscal trends - Trade flow visualizations - Policy effectiveness scores (e.g., change in happiness) *7. Simulation Scenarios* - Universal Basic Income in high-trust society - Collapse in trust - Skilled immigration - Debt-financed infrastructure booms - Trade wars - Austerity vs stimulus - Cultural shifts (e.g., toward collectivism) *8. Extensibility (Future Add-ons)* - Climate change and resource shocks - Technological disruption (e.g., AI unemployment) - Pandemics, war, disasters - Cultural evolution (e.g., post-growth mindsets) On Sun, 6 Apr 2025 at 22:47, Santafe <[email protected]> wrote: > There is an interesting direction here, Steve: > > In the beginning there was whatever-training data, and there was > hallucination with many resonances of things that looked “real” to people > > Practical people (always so ingenuous) wanted to figure out how to sort > through the mess of the training data to get reasoning that was sound, > anchored in whatever “rules of reasoning” are either in the training data > to be pulled out, or else put in by hand because the Practical people have > work they want to get done > > Socially concerned people (this is like a Dylan Thomas poem) were > concerned that, even if the AIs weren’t “hallucinating”, but just trying to > take the best average that could be taken over the mishmash of what was in > the training data, they would still deliver distorted outcomes because the > training data contained a lot of, or even was skewed toward, various > distorted views > > But bad-faith actors do a different thing, for which Glen has taught me > the term of art is “motivated reasoning”. And they (all people) are > wizards at it. They don’t average or coherently hallucinate over the broad > training data, but mask and twist and distort and cull, (and then/in the > process) average or hallucinate over those extractions > > I wonder how much of the subtlety of human cognition is to be found not in > honesty and sense, but in the infinite varieties of bad-faith dishonesty > and nonsense around which “coordination” (so to speak) can be organized. > All happy families etc. Had WIlliam James psychologist lived long enough, > and had wider interests, a second book for him to write > > Will AI designers get to claim a further step toward “understanding human > intelligence” when their creations can spontaneously and autonomously do a > substantive job mimicking the varieties of human dishonesty and bad faith? > > Eric > > > > > On Apr 7, 2025, at 2:47 AM, steve smith <[email protected]> wrote: > > > > new prompt for Trump Apologists: > > > > "Rewrite the ABM in a manner which makes the current US Trade, > Immigration, and DEI policies look like a brilliant move. Push statistics, > charts and rhetoric widely across the internet. Shoot the dog and goat, > deport some folks you don't like the look of and go declare yourself winner > of the golf tournament at your own golf course." > > > > On 4/5/25 3:47 PM, Stephen Guerin wrote: > >> Here's a web version of Marcus's prompt: > >> "write an html/javascript ABM of U.S. trade that considers the deficit, > debt, trade imbalances, and international capital flows." > >> > >> this runs directly in the Claude web context with no downloads or setup. > >> > >> And here is copying the page and deploying to play with the dog. > >> > >> https://guerin.acequia.io/sandbox/marcus-claude-economy.html > >> > >> Old joke applies: ""It's not that the dog talks well, it's that it > talks at all." > >> _________________________________________________________________ > >> Stephen Guerin > >> CEO, Founder > >> https://simtable.com > >> [email protected] > >> > >> [email protected] > >> Harvard Visualization Research and Teaching Lab > >> > >> mobile: (505)577-5828 > >> > >> > >> On Sat, Apr 5, 2025 at 3:24 PM Marcus Daniels <[email protected]> > wrote: > >> • Download Github Copilot. Add Python module. > >> • Get a Claude Console subscription. Select Claude Sonnet 3.7 in > Github Copilot. > >> • Open the Chat window and select Agent. > >> • 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. > >> • Press Run. > >> • Play with dog. > >> > >> From: Friam <[email protected]> on behalf of Pieter Steenekamp > <[email protected]> > >> Date: Saturday, April 5, 2025 at 3:45 AM > >> To: The Friday Morning Applied Complexity Coffee Group < > [email protected]> > >> 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 < > [email protected]> wrote: > >> > >> > >> > >> ---------- Forwarded message --------- > >> From: Computational Social Science Society of the Americas < > [email protected]> > >> Date: Fri, Mar 28, 2025, 7:10 PM > >> Subject: CSSSA April Webinar > >> To: <[email protected]> > >> > >> > >> View this email in your browser > >> > >> > >> > >> 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 > >> > >> > >> > >> > >> > >> > >> 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. > >> > >> Topic: CSSSA April Webinar > >> Time: Apr 2, 2025 10:00 AM Eastern Time (US and Canada) > >> Join Zoom Meeting > >> > https://us02web.zoom.us/j/82181451627?pwd=uYQJrmdphT9pefWvGKbhQgxQby3beG.1 > >> > >> Meeting ID: 821 8145 1627 > >> Passcode: csssa2025 > >> > >> --- > >> > >> One tap mobile > >> +13126266799,,82181451627#,,,,*806775015# US (Chicago) > >> +16469313860,,82181451627#,,,,*806775015# US > >> > >> --- > >> > >> Dial by your location > >> • +1 312 626 6799 US (Chicago) > >> • +1 646 931 3860 US > >> • +1 929 436 2866 US (New York) > >> • +1 301 715 8592 US (Washington DC) > >> • +1 305 224 1968 US > >> • +1 309 205 3325 US > >> • +1 253 205 0468 US > >> • +1 253 215 8782 US (Tacoma) > >> • +1 346 248 7799 US (Houston) > >> • +1 360 209 5623 US > >> • +1 386 347 5053 US > >> • +1 507 473 4847 US > >> • +1 564 217 2000 US > >> • +1 669 444 9171 US > >> • +1 669 900 6833 US (San Jose) > >> • +1 689 278 1000 US > >> • +1 719 359 4580 US > >> > >> Meeting ID: 821 8145 1627 > >> Passcode: 806775015 > >> > >> Find your local number: https://us02web.zoom.us/u/kZvnFw7jt > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> Copyright © 2025 Computational Social Science Society of the Americas, > All rights reserved. > >> You are receiving this email because you have expressed interest in > CSSSA in the past. > >> > >> Our mailing address is: > >> > >> Computational Social Science Society of the Americas > >> > >> 20 Loma Blanca Road > >> > >> Santa Fe, NM 87506 > >> > >> > >> Add us to your address book > >> > >> > >> > >> Want to change how you receive these emails? > >> You can update your preferences or unsubscribe from this list. > >> > >> > >> > >> > >> > >> ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ > ͏ ͏ ͏ ͏ ͏ > >> > >> Error! 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