Intertemporal Choice: Decision Making and Time in Software Engineering
Bibliographic record
Abstract
When making choices in software projects, engineers and other stakeholders engage in decision making that involves uncertain future outcomes. The concept of 'intertemporal choice' describes choices between outcomes at different times in the future. Short-sighted decisions with far-reaching effects are a long-standing cause of concern in the software profession. Common models to support decisions in software projects use concepts such as expected utility and discount factors to quantify future value and enable trade-off decisions. However, a growing body of behavioral research shows that these normative models do not adequately describe how people actually make choices. Our objective is to understand how developers and stakeholders actually take trade-off decisions during software projects that involve current and future benefits, and to identify the human and cooperative factors that influence them. This requires empirical research on decision making in SE with a focus on trade-offs across time. To support such research, this paper reports on a systematic literature review that aimed to identify whether the intersection of these concepts has been acknowledged and addressed. We discuss the assumptions about decision makers that underpin existing research and analyze how the role of time has been characterized in the study of decision making in SE. Based on this review, the paper begins to develop principles for a descriptive framework to characterize intertemporal choices in empirical and behavioral software engineering research.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".