Human Cultural Dimensions and Behavior during COVID-19 Can Lead to Policy Resistance and Economic Losses: A Perspective from Game Theory Analysis
Bibliographic record
Abstract
The recent COVID-19 pandemic has caused significant societal impacts. Besides loss of life there were large additional costs incurred by every country including the treatment of patients and costs to implement response plans. The pandemic resulted in major economic disruptions and stalled growth worldwide due to travel bans, lockdowns, social distancing, and non-essential business closures. Public health officials in almost every country implemented and encouraged Nonpharmaceutical Interventions (NPIs) such as contact tracing, social distancing, masks, and isolation. Human behavioral decision-making concerning social isolation was a major hindrance to the success in curbing the pandemic worldwide. In many developing countries individuals’ choices were motivated by the competing risk of losing jobs, and daily income. In this chapter we focus on human behavior concerning social isolation in the context of decision-making during the pandemic. We developed a conceptual framework and deterministic model that integrated evolutionary game theory within our disease transmission model. We illustrate scenarios numerically simulating the model. This study highlights the idea that human behavior is an important component in successful disease control strategies. Economic resilience, especially in low-income countries, can improve public understanding and uptake of NPIs.
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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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".