Research

Background

Two important aspects to decision-making that formal settings tend to leave implicit are:

Uncertainty in our beliefs. It weaves together the main components of decision-making theory:
- Uncertainty about how to efficiently represent the world
  - How do we abstract the world in a way that minimally distorts our ability to choose actions to achieve a goal?
- Uncertainty about the value of actions we take
  - How does the uncertainty in our beliefs about the world transform into uncertainty about how valuable are the actions we can take in it?
- Uncertainty about what goals we should pursue
  - How (and why!) do we form and select goals to pursue? (ultimately defining what value means for us!)
Why do we sometimes make the effort to reduce these kinds of uncertainty, while other times we simply account for it, or even neglect it outright, even at our peril? While we are typically frugal with cognitive effort, we are also often simply suboptimal with respect to instrumental reward. To what degree is this suboptimality explained by extending our decision utility to include cognitive/informational and affective/social value. We also can misinterpret or are misguided and misinformed by our social environment. That we are social species after all highlights another component.
Social factors, personal values, and sense of identity. They can play a prominent role in the decision-making process within an individual, but also collectively within a society. Many of the pressing societal problems of our time, from polarization to climate change inaction, rest on how we form beliefs while consuming information and dialoguing/sense-making in social venues, increasingly curated by algorithms and populated by agents, on a range of scales from peer-to-peer extending to large, online communities. The form of the information exchanged in these fundamentally social interactions and the form of the mediums over which this exchange occurs can play a role in the collective decisions we make as a society by biasing how individuals decide what to believe.

I am most interested in building and testing data-driven theories about how environmental design filters our uncertainty and social experience into binding social norms that shape what we believe we can and should do. This focus comes from realizing that much of the modern environment is designed and therefore could be designed better.

To this end, I pursue high-impact problems at the intersection of social tech, social science, and AI, supported by mathematical theory and simulation experiments, and always aiming for reproducible scientific analysis and efficient algorithms.

Research Topics

A local (i.e. individual) and global (i.e. societal) approach to understanding social systems highlights two kinds of phenomena:

inference and decision-making in the behaviour of individuals and small groups, and
complex system dynamics of large numbers of interacting learning agents.

I am excited to take part in the modern trend of pushing the theory of games & machine learning towards contact with data to better understand real-world multi-agent systems (e.g. DIMACS workshop series on Bridging Game Theory and Machine Learning for Multi-party Decision Making, and the Social Simulations with LLMs Workshop at COLM 2025), even making modest contributions to theory (e.g. the evolution of social norms)

A list of topics I have studied:

Data-Driven: Large-scale data science and simulations of society

Motivation: better accounting for the effects of the social milieu in which individuals come to believe who they are and what is important to them, my motivation is that we can better align the algorithms that structure our collective discourse, as well as develop prosocial, assistive technologies in that space. In 2022, I organized a RLDM workshop on social alignment in humans and machines on this topic. In this area, I collaborate with psychologists and social (e.g. political) scientists in order to tease apart the interplay of belief, identity, and decision-making bias.

Selected Projects

Using topic correlations inferred from open-ended responses to large scale surveys about climate change beliefs (here about a tax on carbon) to operationalize ideology in pursuit of better climate and policy communication
(Puelma Touzel et al. Environmental Data Science 2024).

Collaborating with the Complex Data Lab, I made contributions to a large-scale analysis of social media data (Yang et al. 2026).

The advent of large langage models has opened up a new and promising approach to studying complex social decision-making phenomena. Using Concordia (Deepmind's LLM-based simulation library for modelling social systems), I lead a FLI-funded project on using simulations to study large-scale manipulation (Puelma Touzel et al. 2024, Puelma Touzel et al. IJCAI 2025).

Theory-Driven: Efficient learning algorithms for environments with realistic properties

My theory work is motivated to advance how we design learning algorithms for use in more realistic settings. While funded under the Canadian Excellence Research Chair in autonomous AI held by Irina Rish (UdeM), I and co-authors mathematically analyzed the subtle dynamics that emerge in multi-agent, continual reinforcement learning settings. Our work frames the challenges in the design of robust algorithms in this setting going forward.

Example Projects

Understanding how, as decision-making problems scale, the time to learn about them grows according to the ways in which the agent and the environment interact.
Continual Learning In Environments With Polynomial Mixing Times
Riemer M*, Chandra Raparthy S*, Cases I, Subbaraj G, Puelma Touzel M, Rish I.
NeurIPS (2022).

The world is full of other behaving and learning agents. How can the complexity of what everyone else is doing be abstracted efficiently?
Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning
Puelma Touzel M*, Me'marian A*, Bhati R, Riemer M, Rish I.
From Cells to Societies: Learning Across Scales Workshop, ICLR (2022).
Scalable Approaches to a theory of many minds
Puelma Touzel M*, Me'marian A*, Riemer M, Mircea A, Williams A, Ahlstrand E, Lehnert L, Bhati R, Dumas G, Rish I.
Agentic Markets, ICML (2024).

Call for co-founders: Social Tech for Climate Crisis

If you have read it this far, you are interested! Hear my pitch for Climate-aware sociology: While we always need to be discerning when evaluating any given idea to ensure it isn't greenwashed by tech solutionism, AI could play role in useful transition tech. People typically have in my mind applications like optimizing measurement sensing, energy efficiency, and BioTech. I actually think the strongest transition tech supported by AI will be social tech: technology that reshapes how we interact with one another to over come coordination failures. If these interest you, get in touch!