Accounting for Risk and Stability in Technology Adoption
Bibliographic record
Abstract
The reasons for sluggish adoption of long‐run agricultural systems are not well understood. Researchers have identified risk and uneven cash flows as two likely culprits, yet the literature has done little to investigate their impacts simultaneously. We explore the unique influence of risk, risk preference, stability, and stability preferences on the adoption of long‐run investments. We developed risk‐stability‐segregated expected utility (RSSEU) to disentangle risk from stability and then compute the impacts of risk, stability, and preferences over a range of values found in previous studies. Results clearly demonstrate that adoption decisions are influenced differentially by risk and stability. An unstable income can overwhelm the risk effect or visa versa, depending on a person's preferences for risk and stability. Disentangling risk and stability could be very important if economists are to understand how decisions are made. For example, we found that the impact of risk on individual behavior is very low when the expected income flow is unstable. In this case, policies that smooth expected income over time will be more effective than ones that reduce risk. In contrast, risky technologies with a stable income path are more appropriately addressed with instruments like insurance or facilitating futures markets. Les raisons de la lente adoption des systèmes agricoles à long terme ne sont pas bien comprises. Selon certains chercheurs, le risque et les flux de trésorerie irréguliers sont deux facteurs probables; toutefois peu de travaux se sont penchés sur leur impact simultané. Nous avons exploré l'influence du risque, des préférences pour le risque, de la stabilité et des préférences pour la stabilité sur l'adoption d'investissements à long terme. Nous avons élaboré une espérance d'utilité distincte risque‐stabilité(risk‐stability‐segregated expected utility) pour dégager le risque de la stabilité et pour calculer l'impact du risque, de la stabilité et des préférences à partir d'une série de valeurs tirées d’études antérieures. Les résultats ont clairement montré que les décisions d'adoption sont influencées différemment par le risque et la stabilité. Un revenu instable peut accabler l'effet du risque ou vice versa, selon les préférences d'une personne pour le risque et pour la stabilité. Si les économistes veulent comprendre de quelles façons les décisions sont prises, il serait important de dégager le risque de la stabilité. Par exemple, nous avons observé que l'impact du risque sur le comportement individuel est très faible lorsque les flux de revenus sont instables. Dans ce cas, les politiques qui adoucissent les revenus prévus au fil du temps seront plus efficaces que celles qui diminuent le risque. En revanche, des instruments tels que les assurances et les marchés à termes sont des moyens plus efficaces lorsque les technologies comportent des risques et que les revenus sont stables.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".