A cross-disciplinary research initiative

The Prism
Network

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One dataset. Fourteen disciplines. Unlimited questions.

A first-of-its-kind collaborative initiative using a digital game - played by real humans - to illuminate some of the most fundamental questions in behavioural and evolutionary science.

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The Big Idea

Science has always been limited by the data it can collect.

🌿
Field Studies
Give us ecological realism but small samples.
🧪
Lab Experiments
Give us control but strip away complexity.
📋
Surveys
Give us scale but lose the moment-to-moment dynamics that actually drive behaviour.

Digital games change this equation entirely.

When a human plays a game designed to explore human behaviour, they are not just playing - they are making thousands of decisions under conditions of genuine uncertainty, resource constraint, social competition, and risk.

The Prism Network is built on a simple but powerful premise: What happens when you hold a large dataset recording individual decisions in full up to the light and let every discipline refract it differently?

Our vision is that collecting data like these at scale would allow us to explore questions at the intersection of evolution, animal behaviour, human psychology, decision theory, computational science, and more. This is why we're inviting research teams from fourteen fields to join our initiative, each bringing their own tools, their own frameworks, and their own questions.

The Game

Meet the game that became
a scientific instrument.

Inglorious Baskers is a survival game developed by Arludo in which players take on the role of a lizard navigating a complex and unforgiving environment. The goal is simple: survive as long as you can. You can play the game here.

🦎
Foraging
Search a dynamic landscape for food to maintain energy levels.
☀️
Basking
Seek out sunny spots to thermoregulate and restore vitality.
🦅
Predator Evasion
Detect and avoid hawks before they detect you.
⚔️
Combat
Fight rival lizards for territory, resources, and dominance.
🦠
Infection
Manage illness and the physiological costs it imposes over time.
📊
Fully Recorded
Every decision, every outcome is logged in full with zero missing data.

But beneath the game lies something remarkable. Every session is a controlled behavioural experiment. Players face the same core trade-offs that real lizards (and real animals of all kinds) face in the wild: energy vs. risk, competition vs. avoidance, short-term gain vs. long-term survival.

The digital environment gives us something field biology rarely can - complete observational coverage of every decision, across hundreds of thousands of interactions, with no missing data.

The game has now been played at scale. What it has generated is one of the most detailed repeated-measures behavioural datasets ever assembled.

The Dataset

The numbers speak for themselves.

123,892
Play Sessions
13,407
Total Players
4,935
Players with Multiple Sessions
~23
Mean Playthroughs per Player
11
Behavioural Traits Measured
3
Game Modes That Alter Player Risk
Eleven behavioural traits are tracked across repeated observations for each individual player - giving the dataset its most unusual property: true within-individual variance at unprecedented scale.

This isn't a snapshot. It's a longitudinal record of how individuals behave, vary, and respond across time and context. The analytical possibilities are vast and deliberately left open for the collaborators who join this initiative.
The Opportunity

This is an invitation,
not a commission.

We are looking for researchers who see something in this dataset that they have never had access to before - and who want to ask the question only their field can ask.

01
Unprecedented Sample Size

Most behavioural datasets struggle to reach the statistical power needed to detect subtle individual differences. This one does not have that problem.

02
Repeated-Measures Structure

Eleven traits measured repeatedly per individual means you can separate within-individual variation from between-individual variation: the holy grail for personality research, life history theory, and cognitive modelling alike.

03
Full Data Transparency

Every collaborating team receives the same complete dataset. No gatekeeping, no hierarchies of access. Fully pre-registered, open science from the ground up.

04
Cross-Disciplinary Co-Authorship

Your analysis sits alongside parallel analyses from thirteen other fields. The synthesis paper alone will be a landmark publication. You'll have read everyone else's work before writing your reflection.

If you have spent your career developing tools, models, or theories that your field has never quite had the data to fully test, then this may be the dataset you have been waiting for.
Fields of Inquiry

Fourteen disciplines.
One shared source.

The Prism Network is looking to bring together researchers from across the behavioural and social sciences, united by a common dataset and a shared commitment to open, collaborative inquiry.

01Animal Personality & Behavioural Syndromes
02Foraging Theory & State-Dependent Behaviour
03Contest Biology & Fighting Strategies
04Life History Theory & Multivariate Selection
05Thermal Biology & Ectotherm Behavioural Ecology
06Ecological & Evolutionary Game Theory
07Predation Risk Ecology & Landscape of Fear
08Behavioural Economics & Decision-Making under Risk
09Scarcity, Poverty & Cognitive Bandwidth
10Computational Cognitive Science & Reinforcement Learning
11Learning, Skill Acquisition & Expertise
12Personality & Individual Differences Psychology
13Game-Based Learning & Learning Analytics
14Network Science & Complex Systems Applied to Behaviour
The Team

Led by researchers who believe the biggest questions require the broadest coalitions.

Prof. Michael Kasumovic
Prof. Michael Kasumovic
Principal Investigator
UNSW Sydney · Founder & Director, Arludo

Michael is an evolutionary biologist whose work sits at the intersection of behavioural ecology, game design, and citizen science. As the founder and director of Arludo, he has long championed digital environments as instruments for generating the kind of data traditional field studies cannot. He leads the Prism Network as Principal Investigator.

Prof. Shinichi Nakagawa
Prof. Shinichi Nakagawa
Co-Investigator
University of Alberta

Shinichi is one of the world's leading experts in meta-analysis, open science, and quantitative methods in ecology and evolution. His work on reproducibility, effect sizes, and within-individual variation has shaped how behavioural science approaches large datasets. He brings statistical architecture and cross-disciplinary rigour to the initiative.

Dr. Pietro Pollo
Dr. Pietro Pollo
Co-Investigator
UNSW Sydney

Pietro's research bridges behavioural ecology and cognitive science, with a focus on how individuals vary in their decision-making under uncertainty. His expertise in individual differences and repeated-measures design is central to how the Prism Network dataset is structured and analysed.

FAQ

Common questions,
straight answers.

Will the core team be authors on every paper?
No. The dataset will be published separately, and each team's paper will reference it. This initiative is about advancing science. It is not an attempt to inflate anyone's publication record. Each paper belongs to the team that wrote it.
How many people can be on a team?
We recommend a team of up to three: the lab supervisor, a postdoctoral researcher, and a PhD student. This keeps teams focused, ensures meaningful contributions at each level, and provides a diversity in thought and experience while also training future researchers.
Where will the papers be published?
We are still working out the details, but our current thinking is a coordinated special issue that brings all contributions together (e.g. Philosophical Transactions). This is part of what makes the Prism Network more than a standard multi-author project - the juxtaposition of fourteen different disciplinary perspectives on the same data will itself be the story.
Can I collaborate with another team?
No, and this is intentional. Each team works independently. The Prism Network is not just a study of behaviour; it is also an examination of how science itself works. How do our training, experiences, and disciplinary perspectives shape what we see in the same data? Independent analyses are essential to answering that question. You will get the chance to read everyone else's work - but only after your own is submitted.
Can I use AI tools in my analysis?
Yes, and it is actively encouraged. Science is changing, and the Prism Network is an opportunity to explore how. By standardising the dataset across all teams, we can examine how AI is used, whether it improves outcomes, and whether it pulls findings toward a common mean. Does AI help? Does it narrow the diversity of approaches? These are open questions. Use it, document it, and reflect on it.
How transparent does the process need to be?
Completely. Pre-register your analysis before you begin. Document every methodological decision. Share any chats you have had with your LLM model. Your paper will conclude with a reflective section: how you used AI, what surprised you, what the process revealed about how your field sees the world, and what it felt like to do science differently. This transparency is not a burden. It's the point.
What happens if multiple researchers from the same field want to join?
This would be ideal! Along with exploring these data from different disciplines, it would also allow us to explore how differences in perspectives and experience affect how we analyse data. With the advent of AI, it is imperitive for us to understand what humans add to the scientific process. Additionally, it allows us to explore how rival labs explore the same dataset - do we have internal biases that affect how we perform science? Are these biases amplified or reduced through the use of AI? These are important questions that only human-led science can answer.
Can I try the game?
Absolutely! Here is a link to the game. You can play this on your computer or on your mobile device (anything that has a browser). And don't worry, your playthrough will not add to the data set. So have some fun exploring the world of Inglorious Baskers!
Why is Arludo - a company - doing this?
Arludo was founded by Prof. Michael Kasumovic 11 years ago with a specific mission: to create game-based experiments that help students learn to think like scientists, while simultaneously collecting anonymised behavioural data at scale for research purposes. The science and the education have always been inseparable. The data collected through Arludo's games (including Inglorious Baskers) is given away freely for research, because that was the point from the beginning. The Prism Network is the most ambitious expression of that mission to date.
Get Involved

Your field. Your questions.
Our data.

If the Prism Network sounds like the collaboration you've been looking for, we'd love to hear from you. We're actively recruiting researchers across all fourteen fields — whether you're a senior investigator looking to extend your theoretical work into new empirical territory, or an early-career researcher seeking a high-impact collaborative project.

Tell us who you are, what field you work in, and why you are interested in taking part and we can send you a summary of the data set so you can start thikning about the question you would want to ask with this dataset.

Get in touch →

The Prism Network is an open science initiative. All collaborating teams will have equal access to the full dataset. All analyses must be pre-registered. We are committed to transparent, reproducible, and inclusive science.