Understanding the Effects of Social Value Orientations in Shaping Regulatory Outcomes through Agent-Based Modeling
An Application in Organic Farming
Bibliographic Data
| ID | 13138299 |
|---|---|
| Authors | Saba Siddiki (0000-0002-5363-2672, Norwegian University of Science and Technology, corresponding author), Christopher Frantz (0000-0002-6105-8738, Norwegian University of Science and Technology) |
| Year | 2023 |
| Volume | 5 |
| Issue | 2 |
| Pages | 203-235 |
| Publication date | 2023-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Review of Public Policy (JOURNAL) |
| Journal identifiers | ISSN: 2679-3873 • E-ISSN: 2706-6274 |
| Publisher | International Public Policy Association (PUBLISHER • FR) |
| DOI | 10.4000/irpp.3398 |
| OpenAlex | W4388143402 |
| Language | EN |
| Citations received | 3 |
| References cited | 38 |
Within existing regulatory scholarship, limited attention is given to whether and how meso-level, or group, characteristics shape compliance. We advance understanding of meso-level regulatory dynamics by assessing how the composition of regulated groups shapes overall compliance levels within a regulated system, as well as compliance trends among system participants. Specifically, we employ agent-based modeling as a tool suited to understanding emergent behaviors to assess how variation in the social value orientations of farmers participating in the United States’ voluntary organic farming regulatory program may shape aggregate and sub-group compliance. We also assess how variation in sanctioning shapes compliance outcomes, shedding light on the interaction between participant motivation and sanctioning mechanisms. We conclude that, for compliance outcomes, the former is more decisive than the latter. The modeling exercise draws on an institutional grammar coding of regulatory design, survey, and interview data. In addition to reporting findings from the modeling exercise in the context of the organic farming regulatory domain, the paper offers insights about leveraging diverse forms of data to inform agent-based modeling, which is particularly appropriate for studying institutional (e.g., policy) and related behavioral dynamics in any governed setting
Business · Compliance (psychology · Context (archaeology · Economics · Knowledge management · Public economics · Scholarship · Computer Science · Global trade, sustainability, and social impact · Insect symbiosis and bacterial influences · Psychology · Regulation and Compliance Studies · Social Psychology
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| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2024 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |