The Development and Validation of a Hierarchical Multiple-Goal Pursuit Model
Bibliographic record
Abstract
Individuals are faced with multiple goals in life, at work, and across these realms every day. Organizational psychologists have begun to address how individuals prioritize goals over time using computational modeling and simulation (e.g., Vancouver et al., 2010). However, they have focused on situations in which an individual must neglect one goal to prioritize another with certainty about the consequences of their actions. Further, the impact of higher-level motivations (e.g., values, identities), on more proximal goal choices remains to be incorporated into dynamic theories of goal pursuit. The current project advances this work by developing a hierarchical multiple-goal pursuit model (HMGPM), which proposes a hierarchical goal system based on Kruglanski and colleagues’ (2002) goal systems theory. The HMGPM specifies qualitatively different levels in this system – means, tasks, and distal goals – and describes the mechanism by which they influence one another via instrumentality. A computational model is specified and subsequently simulated in a virtual experiment. Specifically, contexts are examined in which two tasks can be simultaneously pursued or prioritized one over one another under varying goal network structures and means instrumentality certainties. Specific conditions are then replicated in an empirical repeated-measures experiment in which participants act as university advisors and make schedules for hypothetical students. Simulation and lab study results revealed 1) when individuals have multiple tasks, they prefer a multifinal means that simultaneously accomplishes both, 2) when individuals have a single task, a multifinal means may be less appealing despite its instrumentality, and 3) uncertainty may further drive individuals to maximize their overall likelihood of progress using a multifinal means. Comparisons of the simulation and lab study results revealed 1) the process by which individuals choose means may not simply be driven by a utility-maximization rule at each decision point, and 2) individuals may discount a multifinal means’ instrumentality via a different mechanism than previously theorized (e.g., Zhang et al., 2007). In sum, the current project advances our understanding of how individuals make choices between their many possible actions depending those actions’ consequences, and their ability to predict those consequences, for their multiple goals.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".