Panel 7.1. Model Based Institutional Development

The increasing cognitive demands of digital systems highlight the need for structured ways to assess attention-aware design. In collaboration with the Calm Technology Institute, a framework has been developed that examines human-computer attention relationships across six dimensions: attentional load, peripheral awareness, system reliability, light, sound, and material interaction.

This presentation will cover the development and overview of an 81-point assessment framework that provides specific criteria for evaluating how technological products and services impact human attention. This builds on existing usability evaluation approaches by considering both immediate cognitive demands and background processing requirements. The framework helps analyze how technologies distribute attention demands between focused and peripheral awareness.

By treating attention as a limited resource, the framework proposes metrics for assessing how technical systems affect cognitive load. These criteria provide concrete checkpoints for evaluating design choices that either support or burden human attention.

Early applications of this assessment system have helped identify design elements that influence cognitive demands. These initial findings suggest structured evaluation of attention impacts could support development of more attention-respectful technical systems.

Underneath each of the key principles of successful commons approaches is the efficacy and efficiency of communication. As the complexity of the boundaries, resources, and rules increase, and the diversity of the perspectives, ontology, and goals of participating organizations grows, communications become strained. We tend to reconcile the challenges of noisy inputs using standardization, but standardization comes with tradeoffs to expression and flexibility - and one organization's noise may be another organization's signal. In practice, these tradeoffs are often revealed when organizations work together to collaboratively produce formal information products, such as multi-party grant applications. It is not uncommon for even well-resourced organizations to fail to resolve differences in order to meet deadlines for grant proposals or to simply externalize the process entirely, often at great cost.

This presentation will investigate the insights gained from a 5-year exploration of the development and implementation of protocols for collaborative production of complex information products within an interdisciplinary ecosystem of small-firms, community and university labs, nonprofits and NGOs, communities of practice, and government agencies. Originally driven by the questions of "What happens when the diversity of perspectives is a community's strength and the standardization of communications might undermine it?" and "How can we provide knowledge infrastructure for a community that maintains genuine variety while still allowing for genuine variety and differences in approach, while still reliably allowing for eventual convergence?", the results of this exploration show that, despite the complexity of natural language, best practices from Social Systems Engineering, Memetic Analysis, and Model-Based Systems Engineering allow for protocolization and reliability in communication. The resulting frameworks and insights have broad implications for collaborative knowledge production, safe use of natural language AI, and information exchange between firms with varied requirements and contexts of operation.

The Institutional Grammar (IG) provides a data model for reasoning about how an organization “moves” through time via the underlying behavioral strategies, norms and rules – its institutions. If the position of an organization is described by its team members, activities, resources, and the relationships between them, then a data set of institutional statements can serve as an estimate of that organization’s velocity. It’s possible to extrapolate an informed estimate of an organization’s future position from knowledge about its current position and its velocity (i.e. its rules, norms and strategies for allocating resources), along with beliefs or projections about its future (external) circumstances. The choices that affect an organization’s velocity thus constitute the form of “steering” commonly called “governance”: the application of forces that directly or indirectly induce changes in organizational space.

This presentation explores recent developments in the formalization of the Institutional Grammar for computational methods, and shares learnings from the design and deployment of an ongoing research initiative focused on applying these developments in a real-world context through the experimental implementation of organizational state estimation at an engineering firm. This initiative involves wiring up a feedback system that uses the firm’s internal data infrastructure to continuously sense data about the organization itself, then parses that data via the IG perspective, and makes it visible to members of the firm – who will then be asked to reflect on its accuracy and usefulness. If it is found to be useful, research will shift to questions of dynamics: What is a sensible time scale for such self-observation and modeling? Shifting from a comparative-static view to a view that captures processes of change, and potentially exposes subtle trends not overtly observable? Can a continuous view of an organization’s “velocity” encourage or enable better “steering” and “navigation” – governance of a real world organization?

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.