Session 7. 1. B.

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.

What kind of good is effective decision making within a self-governed organization? How do knowledge commons relate to effective decision making within the operational context where that data is relevant? And what happens if data must flow between different operational contexts (or across organizational boundaries)? This presentation aims to address these questions based on two published papers and practical experience in building infrastructure for and participating in knowledge commons. The first paper “Why is there data?” examines how “data” (and, by extension, “information,” “knowledge,” and “understanding”) form a logical supply supply chain within an operational context in order to support informed decision making inclusive of collective governance decisions. The second paper “Data Mesh Architecture: Interoperability, Co-operation, and Co-Regulation” zooms out to examine data supply chains which extend across operating contexts via technical interoperability, and human coordination across organizational boundaries. The Co-regulatory mesh of organizations offers insights into how the flow of data between organizations with independent governance structures makes visible the existing polycentricity of knowledge commons. These papers have informed development of knowledge organization infrastructure (including but not limited to data sets, microservices, access control regimes, user interfaces, practices and rituals in their use and maintenance). The example cases include a small engineering firm, a non-profit hosting community of governance researchers, and peer-to-peer protocol supporting scientific publishing. The concepts from the papers will be briefly reviewed and then demonstrated via examples.

Most methods of information modeling assume the existence of well-defined structures processes. By contrast, collectives/cooperatives tend to evolve over time, suggesting a more nuanced, contextual approach. While there is vast diversity in cooperatives/collectives, there are also common aspects/activities that open up the possibility of pooling resources.
This paper draws on an ongoing effort to build a no-code platform to enable cooperatives to create their own digital market places. It offers the ability to create web and/or mobile applications and tailor the applications depending on user type. Other, non-digital aspects such as aligning people and building relationships, defining the boundaries and rules, are outside its scope. This paper distills the insights from the experience into four conceptual “pillars” outlined below.
Flexible Data Model & Extensible Architecture
The diverse nature of cooperatives, each having their own structures and processes and goals, makes it imperative to have a flexible data model. Typically, collectives/cooperatives tend to extend their data models as they firm up their roles and processes over time. This necessitates the need for extensibility. Both need to be intrinsic to the effort.
Taxonomy
Once a data model is defined, there is often a need to classify/categorize the data in several different ways. The ability to define the hierarchy of these categories (taxonomy) provides the ability to analyze the data from different perspectives.
Purposeful Activity
Common to cooperatives across areas and types is the idea of “activity” - defined by Activity Theorists as the purposeful interaction of a doer(s) with the world. Activity theory in conjunction with a flexible and extensible architecture enables the platform to include diverse activities at different stages of evolution.
Ostrom’s design principles
This adapts and interprets Elinor Ostrom’s work on the Commons in which she formulated eight design principles (that increase the likelihood of a success) to the digital realm.