SOSIEL: a Cognitive, Multi-Agent, and Knowledge-Based Platform for Modeling Boundedly-Rational Decision-Making

Decision-related activities, such as bottom-up and top-down policy development, analysis, and planning, stand to benefit from the development and application of computer-based models that are capable of representing spatiotemporal social human behavior in local contexts. This is especially the case with our efforts to understand and search for ways to mitigate the context-specific effects of climate change, in which case such models need to include interacting social and ecological components. The development and application of such models has been significantly hindered by the challenges in designing artificial agents whose behavior is grounded in both empirical evidence and theory and in testing the ability of artificial agents to represent the behavior of real-world decision-makers. This dissertation advances our ability to develop such models by overcoming these challenges through the creation of: (a) three new frameworks, (b) two new methods, and (c) two new open-source modeling tools. The three new frameworks include: (a) the SOSIEL framework, which provides a theoretically-grounded blueprint for the development of a new generation of cognitive, multi-agent, and knowledge-based models that consist of agents empowered with cognitive architectures; (b) a new framework for analyzing the bounded rationality of decision-makers, which offers insight into and facilitates the analysis of the relationship between a decision situation and a decision-maker\u27s decision; and (c) a new framework for analyzing the doubly-bounded rationality (DBR) of artificial agents, which does the same for the relationship between a decision situation and an artificial agent\u27s decision. The two new methods include: (a) the SOSIEL method for acquiring and operationalizing decision-making knowledge, which advances our ability to acquire, process, and represent decision-making knowledge for cognitive, multi-agent, and knowledge-based models; and (b) the DBR method for testing the ability of artificial agents to represent human decision-making. The two open-source modeling tools include: (a) the SOSIEL platform, which is a cognitive, multi-agent, and knowledge-based platform for simulating human decision-making; and (b) an application of the platform as the SOSIEL Human Extension (SHE) to an existing forest-climate change model, called LANDIS-II, allowing for the analysis of co-evolutionary human-forest-climate interactions. To provide a context for examples and also guidelines for knowledge acquisition, the dissertation includes a case study of social-ecological interactions in an area of the Ukrainian Carpathians where LANDIS-II with SHE are currently being applied. As a result, this dissertation advances science by: (a) providing a theoretical foundation for and demonstrating the implementation of a next generation of models that are cognitive, multi-agent, and knowledge-based; and (b) providing a new perspective for understanding, analyzing, and testing the ability of artificial agents to represent human decision-making that is rooted in psychology

The Doubly-Bounded Rationality of an Artificial Agent and its Ability to Represent the Bounded Rationality of a Human Decision-Maker in Policy-Relevant Situations

This article introduces two tools aimed at improving our understanding of the relationship between human and artificial rationality and helping us identify agents that are false positives or negatives. The first is a framework that systematically exposes where and how discrepancies between human and artificial rationalities can arise. The second is a test that utilises the insight gained from applying the framework in testing the ability of an artificial agent to represent human decision-making. To demonstrate the usefulness of the test, the article describes its application in testing the ability of a set of Individual Evolutionary Learning agents to represent human decision-making in a social psychology experiment, called the Voluntary Contributions Mechanism. In contrast to the results of a prior test that relied on a behaviour-based method, the results of this test show that the ability of these artificial agents to replicate the behaviour of their human counterparts is not a reliable indicator of their ability to represent their decision-making. The article then uses insight from the test to suggest how to improve the ability of Individual Evolutionary Learning agents to represent human decision-making in the Voluntary Contributions Mechanism

Adoption of Prosocial Common Pool Behavior

New theoretical agent-based model of population-wide adoption of prosocial common-pool behavior with four parameters (initial percent of adopters, pressure to change behavior, synergy from behavior, and population density); dynamics in behavior, movement, freeriding, and group composition and size; and emergence of multilevel group selection. Theoretical analysis of the model\u27s dynamics identified six regions in the model\u27s parameter space, in which pressure-synergy combinations lead to different outcomes: extinction, persistence, and full adoption. Simulation results verified the theoretical analysis and demonstrated that: increases in density reduce number of pressure-synergy combinations leading to population-wide adoption; initial percent of contributors affects underlying behavior and final outcomes, but not size of regions or transition zones between them; and random movement assists adoption of prosocial common-pool behavior.https://pdxscholar.library.pdx.edu/systems_science_seminar_series/1113/thumbnail.jp

A New Agent-Based Model Offers Insight into Population-Wide Adoption of Prosocial Common-Pool Behavior

New theoretical agent-based model of population-wide adoption of prosocial common-pool behavior with four parameters (initial percent of adopters, pressure to change behavior, synergy from behavior, and population density); dynamics in behavior, movement, freeriding, and group composition and size; and emergence of multilevel group selection. Theoretical analysis of model’s dynamics identified six regions in model’s parameter space, in which pressure-synergy combinations lead to different outcomes: extinction, persistence, and full adoption. Simulation results verified the theoretical analysis and demonstrated that increases in density reduce number of pressure-synergy combinations leading to population-wide adoption; initial percent of contributors affects underlying behavior and final outcomes, but not size of regions or transition zones between them; and random movement assists adoption of prosocial common-pool behavior

A New Agent-Based Model Provides Insight Into Assumptions in Modeling Forest Management Under Deep Uncertainty

Context: Exploratory modeling in forestry uses a variety of approaches to study forest management questions. One key assumption that every approach makes is about the degree of deep uncertainty—the lack of knowledge required for making an informed decision—that future forest managers will face. This assumption can strongly influence simulation results and the conclusions drawn from them, but is rarely studied. Objectives: Our objective was to measure the degree of deep uncertainty within a forest management simulation to compare alternative modeling approaches and improve understanding of when a specific approach should be applied. Methods We first developed a method for measuring the degree of deep uncertainty assumed by approaches to modeling forest management. Next, we developed a new extension to the LANDIS-II model, the SOSIEL Harvest Extension, which simulates alternative approaches to modeling forest management. Finally, we applied the new method and extension to comparing three alternative approaches to modeling forest management in Michigan. Results: The degrees of deep uncertainty varied substantially among the three modeling approaches. There is also an overall negative relationship between the degree of deep uncertainty an approach assumes a forest manager will face and the level of flexibility the approach assumes a manager will have in responding to forest change. Conclusions Quantifying the deep uncertainty inherent in simulated forest management and comparing it across models provides an opportunity to better understand its sources and investigate differences in the assumptions made by alternative modeling approaches

A global assessment of policy tools to support climate adaptation

Governments, businesses, and civil society organizations have diverse policy tools to incentivize adaptation. Policy tools can shape the type and extent of adaptation, and therefore, function either as barriers or enablers for reducing risk and vulnerability. Using data from a systematic review of academic literature on global adaptation responses to climate change (n = 1549 peer-reviewed articles), we categorize the types of policy tools used to shape climate adaptation. We apply qualitative and quantitative analyses to assess the contexts where particular tools are used, along with equity implications for groups targeted by the tools, and the tools’ relationships with transformational adaptation indicators such as the depth, scope, and speed of adaptation. We find diverse types of tools documented across sectors and geographic regions. We also identify a mismatch between the tools that consider equity and those that yield more transformational adaptations. Direct regulations, plans, and capacity building are associated with higher depth and scope of adaptation (thus transformational adaptation), while economic instruments, information provisioning, and networks are not; the latter tools, however, are more likely to target marginalized groups in their design and implementation. We identify multiple research gaps, including a need to assess instrument mixes rather than single tools and to assess adaptations that result from policy implementation

A systematic global stocktake of evidence on human adaptation to climate change

Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses