Although commons scholars usually attend to the social structures, processes, and mechanisms of a common pool resource management institution, the skills and training of individual members and users is also a vital component of community governance of natural resources. In this panel we invite a conversation around ontologies for sustainable resource management skills, analysis and evaluation of education programs, and design of training and leadership programs that support commons management regimes. With this panel, we focus on the individual, psychological, and personal development dimensions of community managed institutions. Relevant themes include traditional and contemporary approaches to training, the role of leadership, the psychology of commons stewardship, and education and the commons.
Low-lying coastal communities are disproportionately vulnerable to coastal climate hazards that jeopardize livelihoods, health and wellbeing, heritage and connection to place, ocean-reliant economies, and critical infrastructure. To support communities in advocating for and making informed decisions about climate resilience in places they live, work, play in and rely on, ensuring information and data are accessible and usable for diverse users is critical. But how is this actually achieved? This presentation will describe a multi-pronged approach being implemented as part of a trans-disciplinary, community-engaged coastal climate resilience project in New England working waterfront communities geared at democratizing access to and the usability of information. This approach includes 1) the development of an ‘accessibility instrument’ against which to evaluate information/knowledge/data; 2) the creation of multimedia communication products to help researchers translate coastal resilience tools and data being produced for this project to heterogeneous audiences; 3) semi-structured community focus groups on data accessibility and information/knowledge gaps. Though this project is still in its early phases of implementation, we will discuss barriers and drivers to information accessibility internal and external to researcher’s sphere of influence; challenges and successes related to the co-development of relationships, networks, information; and themes including but not limited to data ownership in frontline communities, the hyper-localization of information, and the influence of researcher positionality and community context. We welcome feedback, discussion, and stories from attendees related to their own experiences making information more accessible to diverse audiences.
Getting a group to adopt cooperative norms is an enduring challenge. Such cooperation is even harder for groups that interact across multiple environments. Groups of strangers often have to attain cooperative outcomes across a range of environments. We introduce a laboratory setting to test if groups can guide themselves to cooperative outcomes by manipulating the environmental parameters that shape their own emergent cooperation process. We test for cooperation in a set of games that impose different social dilemmas. These games vary in stability, efficiency, "alignment", and fairness. By offering agency over behavior along with second-order agency over the rules of the game, we understand emergent cooperation in naturalistic settings in which the rules of the game are themselves dynamic. The literature on transfer learning in games suggest that interactions between features are important and might aid or hinder the transfer of cooperative learning to new settings.
Understanding the processes that lead to the emergence and continued adaptation of institutions remains the raison d’être for the field of institutional analysis. While extant theory provides explanatory accounts for such processes, their postulates build on sampled empirical cases, commonly with emphasis on comparative-static information as well as context-specific arrangements and outcomes (e.g., Ostrom) or primarily theoretical engagement (e.g., Aoki).
One alternative approach to develop and test theories of collective action is to rely on computational modeling, which is carried by promises of full control of scenario specification as well as comprehensive data collection of antecedents, processes and outcomes. Previous work approached this challenge by variably focusing on norm emergence and inference (e.g., Axelrod (1986), Frantz et al. (2015)), the experimental parameterization of rules in commons scenarios (e.g., Smajgl et al. (2008), Ghorbani and Bravo (2016)), or explored it in support of real-world experimentation (e.g., Janssen and Ostrom, 2006).
Addressing the gap of capturing the institutionalization process comprehensively, we develop a cognitively plausible, yet transparent, architectural model of institutional agents that are able to capture the entire strata of institutions, as well as exhibit the associated capabilities to drive associated formation and transition processes. This includes the exploration, inference and adaption of behavioral strategies, the inference and socialization of behavioral norms as well as endogenous introduction and adaptation to novel rules.
To this end, the institutional agent model recognizes principles of experiential and observational learning, memorization, differentiated institutional reasoning, implicit social cognition, as well as autonomous decision-making.
Drawing on a moderately complex scenario consisting of different actor types and action spaces, we illustrate the principal functioning of this architecture in a social setting. Exploring the effects of varying social choice mechanisms (e.g., voting variants) and configurations, we a) identify conditions that drive variation in process and outcome, and b) compare the varying outcomes caused by different social choice mechanisms (and compositions thereof).
Captured under the umbrella of “Generative Institutional Analysis”, we highlight opportunities for the purpose of developing and testing theory using computational institutional models, as well as outlining opportunities for the use in empirical settings.
