[FRIAM] CSSSA April Webinar

Pieter Steenekamp pieters at randcontrols.co.za
Mon Apr 7 00:29:47 EDT 2025 

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 <desmith at santafe.edu> 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 <sasmyth at swcp.com> 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
> >> stephen.guerin at simtable.com
> >>
> >> stephenguerin at fas.harvard.edu
> >> Harvard Visualization Research and Teaching Lab
> >>
> >> mobile: (505)577-5828
> >>
> >>
> >> On Sat, Apr 5, 2025 at 3:24 PM Marcus Daniels <marcus at snoutfarm.com>
> 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 <friam-bounces at redfish.com> on behalf of Pieter Steenekamp
> <pieters at randcontrols.co.za>
> >> Date: Saturday, April 5, 2025 at 3:45 AM
> >> To: The Friday Morning Applied Complexity Coffee Group <
> friam at 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.guerin at simtable.com> wrote:
> >>
> >>
> >>
> >> ---------- Forwarded message ---------
> >> From: Computational Social Science Society of the Americas <
> newsletter at computationalsocialscience.org>
> >> Date: Fri, Mar 28, 2025, 7:10 PM
> >> Subject: CSSSA April Webinar
> >> To: <stephen.guerin at simtable.com>
> >>
> >>
> >> 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
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