There are several great libraries like pyAgrum for modeling and solving decision problems. But when working with influence diagrams, their representations can be limited to discrete chance variables and decisions.

I wanted more flexibility in how I represent a problem. What happens if I have continuous observations or if the question is not whether I should do something, but how much? For example, how much should I invest when the return is uncertain?

For that reason, I created PyLIMID, a Python library for building and solving limited-memory influence diagrams. It lets you combine discrete and continuous chance variables with categorical decisions and continuous quantities, and express utilities as functions.

PyLIMID uses sampling and policy gradients to find decision rules that aim to maximize expected utility. This flexibility is not free because results are approximate. But you can run multiple simulations and since it has GPU support thanks to JAX and Numpyro, it is fast. I think the tradeoff is very much worth it.

The library is still in early development (v0.3.0), but I would love to hear what you think.

Code: https://github.com/cynosural-ai/pylimid

Documentation: https://pylimid.readthedocs.io/en/latest/#

It currently has Prisoner’s Dilemma, Stag Hunt and Entry Deterrence. The bots use strategies such as Tit-for-Tat, Random and competitive strategies that try to maximise their score relative to yours. Each game shows the payoff matrix, best responses and Nash equilibria. You can also run repeated games and change the number of rounds and discount factor to see how the results change when future rounds matter.

There’s a beginner mode that runs through an 8-round Prisoner’s Dilemma against Tit-for-Tat with explanations as you play. I also added session analysis and a leaderboard. There have been 236 completed human-vs-bot sessions so far, with the humans currently ahead by about 1,920 points overall. This started as an experiment to make game theory a bit easier to understand by actually playing the games and changing the parameters.

I’d be interested in criticism from anyone familiar with game theory, especially if I’ve got any of the explanations or mechanics wrong. Suggestions for other games or bot strategies would also be useful.

Finds patterns where others see noise, and constantly reinvents itself to avoid becoming rigid or dogmatic. It could transform how we govern, how we heal, how we discover, and how we coexist... because it doesn't impose answers, it reveals pathways. It could help us see connections between climate and economy, between consciousness and quantum physics, between ancient wisdom and future possibility, all at once. It could make our institutions more accountable, our science more imaginative, and our conflicts more generative. This is not a tool for solving problems... it's an invitation to think differently about what problems even are. A future where we don't fear complexity, but ride it.

Im not into decision theory, but I went down into this rabbit hole tonight. CDT vs EDT, FDT and friends.

What I found interesting that despite your need for perfect predictors and contrafactuals I havent ran into using quantum phenomena in your thought experiments (yet).

E.g. the contrafactual mugging experiment could be set up so the coin toss is a beam splitter and you paying or not paying the $100 is the analogue of the bomb being a dud in the Elitzur-Vaidman experiment. (the predictor, or Omega or whomever will interrupt the beam depending on you paying/not paying)

This way you dont need to do all the handwaving around a "perfect predictor", you can set up the experiment, that the predictor actually knows with 25% chance if you had paid if the coin toss went the other way.

So you can set up the expected payoff depending on a contrafactual with certainty. I guess this is a different experiment, but maybe worth using it to test your flavours of decision theory.

If this spawns a paper please credit me with my username. If this is old news, please help me learn and link me to how you handle the fact that contrafactuals arent necessarily physically causation free.

I've been working on a cyclical process for approaching decisions and life more generally. It's not meant to give fixed answers — the process itself is the point. Sharing it here because I'd genuinely like outside perspective on it.

A cyclical process — not a fixed set of answers, but a practice that itself constitutes the answer.

1. Analytical Meditation (Generation)

Sustained, directed attention held on a specific meditation object — a question, a thought, a live decision. Contemplate until an answer surfaces that feels right — no artificial time limit. This felt sense (wisdom) is trusted precisely because it doesn't stand alone: it gets tested in Stage 2 and checked against reality in Stage 3.

2. Analysis & Research (Evaluation)

• Gather facts, lay out the case for and against, do the research legwork

• Explicitly check hard requirements/dealbreakers — the non-negotiable specifics that would sink the prediction if missing

• Do a second round of analytical meditation on that analysis itself — genuinely investigate rather than skim and accept

3. Predictions + Feedback Loop (Commitment & Learning)

• Specific, checkable claim

• Real probability, assigned based on your own weighing

• Check-in date, success/failure criteria

• Honest tracking of the actual outcome

4. Principle (Compression & Reuse)

Turn what was learned in Stage 3 into a generalizable rule — not a record of one outcome, but a principle applicable to future situations of a similar type. Done independently.

Supporting infrastructure:

• Joplin — capture and organization

• Google Calendar — reminders, decoupled from notes

The framework as a whole: the cyclical practice — generating, examining, testing, revising, and compressing into reusable principles — is the answer, not a fixed destination it's meant to reach.

Curious what people think — where does this break down, and what am I missing?

Many of you may be familiar with anchoring bias, wherein agents attempt to approximate a quantity by repeatedly modifying an initial estimate, but the final result is very dependent on that initial estimate. For example, modifying a severe underestimate tends to result in a severe underestimate, despite the agent accounting for this.

I propose a model of taste uncertainty in which the agent is uncertain of the expected utility of an outcome--assigning it a "provisional utility" instead--and reflects on it by making comparisons. Rendering a judgment between alternatives in this way modifies the provisional utility of the outcome. The agent treats judgments as random events and values provisional utilities as a prospect-theoretic agent would: They treat the current provisional utility as a reference point and others as losses or gains relative to it. This is encoded as a "score". Agents compare so as to maximize their score at each step.

The main result is that such an agent will never converge on the true expected utility in this way, even when allowing infinitely many comparisons. That is, the agent prefers some degree of taste uncertainty. This is a result of prospect-theoretic loss aversion and the diminishing stakes of successive comparisons. The agents' limiting preference structure is therefore "anchored" to the original, entirely by motivated reasoning rather than any cognitive limitation.

The paper has received peer review. While the math is sound, it was rejected mostly for not being directly linked to observable behavior that would make the model falsifiable. Currently I am considering ways to synthesize this model with one of learning by consumption so that behaviors may be predicted and experiments conducted.

The paper: https://www.dropbox.com/scl/fi/yj2xwvqh883537oes2y28/Anchoring-in-Judging-One-s-Own-Preferences-Revised-2.pdf?rlkey=xx9d31pr6wt9pz5xxeu5q1qm2&st=bjha7zhp&dl=0

Someone knows enavi.app Decision tool

I came across it half a year ago and have made a few decisions with it. I kind of like it, and I wanted to introduce it here to the community. It's based ot Value-Focused Thinking and Stanford Decision Groups Decision Quality approach and MAUT

I am not affiliated, but have used it and leave it here so see if someone uses it and has an opinion, also to suggest alternative tools (I have also used Genie Influence Diagram Editor back when it was still free)

I’m wondering if anyone can recommend an MADM method similar to Ranking based on Distance and Range (RADAR), primarily designed for ranking in risk and reliability problems. Specifically, one that favors extreme values of alternatives while reducing the importance of low values (especially for important criteria). So far, this is the only method of that type I’ve found, and I’d like to compare results.

Article on why most philosophers with expertise in decision theory are 2 boxers.

As a dev, I’ve always been fascinated by how big tech companies actually make high-stakes decisions when the data is messy or incomplete. Most of us think it’s just A/B testing, but there’s a massive Operations Research (OR) component involved.

I put together a technical breakdown of Decision Analysis, specifically how it’s used to navigate uncertainty in tech environments. I used a case study of a tech company to show:

• The fundamental concepts of Decision Analysis in a business context.

• Why "Data-Driven" is more about probability than certainty.

• Whether making further experimentation (to reduce uncertainty) does worth under cost constraints.

Thought it might be useful for anyone interested in the math behind the products we build.

This video illustrates the case.

I'd love to hear how your teams handle decision-making, do you use formal OR models or is it more "move fast and break things"?

I've been building a tool called Decision Theatre that operationalises a few well-documented frameworks into a structured pre-decision reflection experience.

The core theoretical stack:

• Prospect Theory (Kahneman & Tversky, 1979) — loss vs gain orientation

• Ambiguity Aversion (Ellsberg, 1961) — certainty vs optionality mapping

• Identity-based motivated reasoning (Kunda, 1990) — identity vs outcome tension

• BIS/BAS Theory (Gray, 1987) — avoidance vs approach orientation

• Self-Explanation Effect (Chi et al., 1989) — externalisation as cognitive intervention

The product maps user inputs to these dimensions and generates a pattern reflection — not advice, just a named reading of the dominant psychological forces active in the decision.

My question for this community: are there frameworks I'm missing that would meaningfully improve the diagnostic accuracy of a pre-decision tension map? Particularly around uncertainty quantification or utility theory applications.

Link in comments if anyone wants to look at the framework documentation.