Choice Conditions and Behavioural Leverage Points in Complex and Adaptive Systems
Bibliographic record
Abstract
Applied behavioural design solutions and behavioural public policy interventions often start with the presumption that the environments into which they will be placed are stable, yet many behavioural policies and interventions occur within complex system contexts that are likely to change over time. As a result, while creating targeted, evidence-based solutions to address discrete behaviours in users’ immediate environments—often referred to as improved ‘choice architecture’—can help achieve directed behavioural change, behavioural design practitioners are also likely to benefit from new strategies that can help them see how larger system forces will impact (or be impacted by) new solutions. This suggests that designing for behaviour within complex systems may benefit from ways to understand how system and institutional ‘plumbing’—the underlying choice conditions, or ‘choice infrastructure’— enables and shapes behaviours, as well as how design efforts must recognise where behaviours, judgement, and decision-making are constrained by broader cultural or institutional ideologies that contribute to underlying paradigms, norms, and belief systems. This paper proposes a model that practitioners can use to systematically capture various system forces that may impact human and system behaviour, as well as how these tensions might influence each other or support emergent behaviours and system conditions over time. The model takes the form of a matrix created by two complementary dimensions: first, the progressive scale of micro, meso, and macro system levels, and second, the increasingly embedded nature of system activity and mechanisms that build from targeted instances and interventions to underlying infrastructures that indirectly support behaviours, to ideological influences on behaviour in the form of mental models or belief systems that bound and shape behaviour within systems. After describing the rationale and conceptual model for this view, the paper uses the example of responsible research assessment (RRA) reform as an illustrative case. It concludes by suggesting how further interrogating the matrix content can provide new opportunities for behavioural systems design, helping practitioners explore the notion of system stability in complex system contexts, determining where potential areas of leverage may exist in the form of emergent activity or ‘hotspots’, and preventing against the potential of unintended consequences or inaction.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".