Games for experiential learning: triggering collective changes in commons management
Bibliographic record
Abstract
As resource users interact and impose externalities onto each other, institutions are needed to coordinate resource use, create trust, and provide incentives for sustainable management. Coordinated collective action can play a key role in enabling communities to manage natural resource commons more sustainably. But when such collective action is not present, what can be done to foster it? We contribute to the understanding of how experiential learning through games can affect behavioral change, potentially leading to more sustainable commons management. We present a conceptual framework describing the most important processes involved in experiential learning games. The framework highlights the importance of the game context for achieving game outcomes. We list game features that have been argued to influence learning and behavioral determinants, focusing on the game narrative and experience, game rules, and attributes of players. We briefly describe how each game feature influences the processes in the framework. Next, we apply the conceptual framework to examine design features that were particularly important for influencing behavioral drivers in commons management in three intervention cases from India relating to groundwater, surface water, and forests. Our conceptual reflections underpin the need to debate about underlying assumptions in using games as intervention tools. Making assumptions transparent can help to understand why or under what conditions experiential learning works or fails. There is a critical need for more systematic choices of the right tools for the right purpose. This includes participants of the experiential learning who must be able to relate the game to their real life. A social dilemma in the game should at least, in its basic structure, represent a real-life dilemma. We close by highlighting future research needs both from the conceptual behavioral change as well as the game design perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".