Grim FATE: Learning About Systems Thinking in an In-Depth Climate Change Simulation
Bibliographic record
Abstract
Background. Simulations of complex systems have a long history of use for the study and promotion of systems thinking , yet more can be done in identifying games that promote development of systems thinking . Aim. This study is an exploration of the hypothesis that FATE OF THE WORLD, a challenging and complex climate change simulation , can promote systems thinking about climate change. Questions. This article analyzes players’ engagement with FATE OF THE WORLD using three key questions : 1. In what ways does the game support thinking about climate change as a complex system? 2. Does the game correspond to players’ a priori model of climate change? 3. How do players relate to FATE as an artifact they embrace, critique, and tinker with? Method. 33 participants were matched into control and test groups , and experimental participants were assigned to play a full game scenario of FATE OF THE WORLD. Experimental and control groups were compared using pre-and-post intervention concept maps . Post interviews were conducted with the test group. Results. Concept maps revealed statistically significant differences between the control and test groups. Interviews revealed diversity in learning outcomes and the ways in which acceptance of the game’s model of climate change influenced learning. Conclusions. FATE serves as proof-of-concept for the power of complex simulations to promote systems thinking as well as in-depth reflection on key social challenges . However, simulations like FATE are unlikely to serve well as stand-alone educational tools, which highlights the importance of effective teaching to accompany the game.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".