Complex systems such as cities, energy grids, or the global climate have many plausible futures. Scenarios, or structured narratives of decision-relevant futures, are a common decision support tool for making the complexity and uncertainties of complex systems humanly interpretable. However, the effectiveness of scenario-based decision support depends in part on the usefulness of the selected scenarios. Here we show an optimization-based approach for generating scenarios that are specifically designed to be diverse, plausible, and comprehensive. We establish the advantages of our method by evaluating it against three previously proposed methods: scenario matrices, generic archetypes, and clustering. Our case study is Schelling’s segregation model, a tractable yet behaviorally rich simulation of a complex system. Our results show the proposed optimization-based approach can generate more diverse, plausible, and comprehensive scenarios than existing approaches. The resulting scenarios may provide a more insightful and robust basis for policy decisions, especially for complex systems with emergent behavior or where substantial uncertainties are present.

Propositions1.The use of single metrics will not progress our research on resilience.(this thesis) 2. Scenarios for decision support should be based on plausibility, not probability.(this thesis) 3. Discoveries should not be named after their discoverer(s).4. The most significant questions in artificial intelligence research can only be answered by philosophers. The widespread availability of Large LanguageModels such as ChatGPT will increase the rate of societal in-person interactions.6. Uncertainties and simplifications should be emphasized more when communicating scientific advances to the general public.

Our ability to deal with external changes is determined by our collective willingness to transform and adopt new technologies. These factors are driven by people's opinion on the change itself and the proposed policies. Humans constantly update their opinion by integrating new information they hear with their values, which helps them make a judgement about that new information. Here, we create an agent -based model that explicitly incorporates the concept of values to explore possible drivers of opinion dynamics. In the model, we explore several factors and perform local and global sensitivity analysis to test their individual and interaction effects. We find that consensus formation in the model is mainly determined by factors related to (1) the amount of stochasticity in the opinion updating procedure and (2) the relative ease with which old links are removed and new links are created. Our results demonstrate how opinions and values may co -evolve. Furthermore, they may help in understanding human responses to new policies such as covid-related restrictions or calls to shift to a more plant -based diet.

An increased interest in the resilience of complex socio-ecological and socio-technical systems has led to a variety of metrics being proposed. An overview of these metrics and their underlying concepts would support identifying useful metrics for applications in science and engineering. This study undertakes a scoping review of resilience metrics for systems straddling the societal, ecological, and technical domains to determine how resilience has been measured, the conceptual differences between the proposed approaches, and how they align with the domains of their case studies. We find that a wide variety of resilience metrics have been proposed in the literature. Conceptually, ten different quantification approaches were identified. Four different disturbance types were observed, including sudden, continuous, multiple, and abruptly ending disturbances. Surprisingly, there is no strong pattern regarding socio-ecological systems being studied using the “ecological resilience” concept and socio-technical systems being studied using the “engineering resilience” concept. As a result, we recommend that researchers use multiple resilience metrics in the same study, ideally following different conceptual approaches, and compare the resulting insights. Furthermore, the used metrics should be mathematically defined, the included variables explained and their units provided, and the chosen functional form justified.

Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.

Issues of scale pervade every aspect of socio-environmental systems (SES) modeling. They can stem from the context of both the modeling process, and the purpose of the integrated model. A webinar hosted by the National Socio-Environmental Synthesis Center (SESYNC), The Integrated Assessment Society (TIAS) and the journal Socio-Environmental Systems Modelling (SESMO) explored how model stakeholders can address issues of scale. Four key considerations were raised: (1) being aware of our influence on the modeling pathway, and developing a shared language to overcome cross-disciplinary communication barriers; (2) that localized effects may aggregate to influence behavior at larger scales, necessitating the consideration of multiple scales; (3) that these effects are “patterns” that can be elicited to capture understanding of a system (of systems); and (4) recognition that the scales must be relevant to the involved stakeholders and decision makers. Key references in these four areas of consideration are presented to complement the discussion of confronting scale as a grand challenge in socio-environmental modeling. By considering these aspects within the integrated modeling process, we are better able to confront the issues of scale in socio-environmental modeling.

Complex systems can exhibit autopoiesis–a remarkable capability to reproduce or restore themselves to maintain existence and functionality. We explore the resilience of autopoietic patterns–their a...

Scenario Discovery is a widely used method in model-based decision support for identifying common input space properties across ensembles of exploratory model runs. For model runs with behavior over time, these properties are identified by reducing each run to a single value, which obscures potentially decision-relevant dynamics. We address the problem of considering dynamics in Scenario Discovery by applying time series clustering to the ensemble of model runs, and then finding the common input properties for each cluster. This separates the input space into multiple scenarios, each corresponding to a distinct model dynamic. Policy interventions can be targeted at different scenarios by analyzing overlap of these subspaces. Our work expands Scenario Discovery by improving consideration of system behavior over time, which is highly relevant for the management of complex nonlinear systems such as ecosystems or technical infrastructure.

This manuscript provides information for replicating the Coasting agent-based model presented in “Simulating emerging coastal tourism vulnerabilities: an agent-based modelling approach”. The model description follows the Overview, Design Concepts, and Details + Human Decision-making (ODD+D) protocol. Moreover, this paper includes implementation details on global sensitivity analysis and scenario discovery. Finally, we provide supplementary tables and figures for scenario discovery results not included in the main paper.Highlights: • Model description for simulating emerging environmental vulnerabilities in a coastal tourism context • Coasting’s design facilitates model adaptations to other coastal tourism destinations • Implementation details for applying global sensitivity analysis and scenario discovery to vulnerability assessments

Coastal tourism destinations face a range of climate-related changes. Prevailing challenges include understanding emerging changes and future uncertainties. A dynamic vulnerability approach is a promising way to analyse emerging socio-ecological vulnerabilities. This research presents an innovative coupling of the human-environment system in the agent-based model Coasting, and is applied to Curaçao's coastal tourism. We observe how operator numbers and environmental attractiveness, proxies for socio-ecological vulnerabilities, change over time. Global sensitivity analysis highlights the main interacting factors behind socio-ecological vulnerabilities. Scenario discovery explores the main drivers contributing to undesirable vulnerabilities. The model's findings provide key insights on which factors tourism destinations need to focus on to prevent socio-ecological vulnerabilities.

(An) Agent-Based Model of a National Housing Market Authors: Ian Lim, John S Schuler 4 (A) Breeding Pool of Ideas: Analyzing Interdisciplinary Collaborations at the Complex Systems Summer School Authors: Jacqueline Brown, Dakota Murray, Kyle Furlong, Emily Coco, Fabian Dablander 5 Complex Systems Science and Community-Based Research: A Scoping Review Protocol Authors: Travis R Moore, Helena VonVille, Winnie Poel, Glory Dee A Romo, Ian Lim, Robert W S Coulter 6 CSSSSSSSSS: Complex Systems Summer School Selected Social Survey Statistics, Summarized Succinctly Authors: Shihui Feng, Kate Wootton, Alec Kirkley, Hunter Wapman 7 Cultural erosion in online communities Authors: Marjoriikka Ylisiurua, Winnie Poel 8 Disentangling ecological and taphonomic signals in ancient food webs Authors: Jack Shaw, Kate Wootton, Emily Coco, Dries Daems, Andrew Gillreath-Brown, Anshuman Swain 9 Do decision strategies naturally emerge as a result of the brain’s necessity for efficient coding? Authors: Paula Parpart, Mikaela Akrenius 10 Entropy in mountainous river networks Authors: Gen Li, Alec Kirkley, Dan Krofcheck, Brennan Klein 11 Evolution of phenotypic diversity and co-operation in a spatially explicit model of microbial populations Authors: Jessica Audrey Lee, Kirtus Leyba, Adam Z Reynolds, Ritwika VPS, Daniel Borrero, Pam Mantri 12 Food Security and Resilience: A New Paradigm Authors: Erwin Knippenberg, Andrew Gillreath-Brown, Dan Krofcheck, Pam Mantri, Fabian Dablander, Alexander Bakus 13 (The) Influence of education on the aesthetic perception of strange attractors: A pilot study Authors: Mikaela Akrenius, Ethan Nadler, Mark Chu 14 Lilliput Effect: Explaining mass extinction driven dwarfing with metabolic scaling Authors: Anshuman Swain, Jordi Piñero, Jack Shaw 15 Long-scale language dynamics as a reaction–diffusion system: mesoscopic analysis Authors: Henri Kauhanen, Ritwika VPS, Harun Šiljak, Kenzie Givens, Pablo M Flores 16 Lost in Translation? Evaluating the Gap in Science Policy Communication Authors: John F Malloy, Dakota Murray, Ritwika VPS, Christina Boyce-Jacino, Kyle Furlong, Mackenzie M Johnson, Andrew Gillreath-Brown 17 Microbiobots: gene transfer and evolution in a genetically diverse robot swarm population Authors: Levi Fussell, Kirtus Leyba, Jessica A Lee, Anshuman Swain 18 Models and Metaphors: How to use these heuristic devices in science and decision making? Authors: Dries Daems, Doug Reckamp, Ethan Nadler 19 Overweighted Expectation: A Psychological Explanation for the Financial Crisis Authors: Mikaela Akrenius, Elissa Cohen, Ahyan Panjwani 20 Predicting Population Density Based on Water Resource Availability Authors: Jessica Brumley, Catherine Brinkley, Gen Li, Ian Lim 21 Putting humans in food webs Authors: Alexander Bakus, Erwin Knippenberg, Chris Quarles, Patrick Steinmann, Kate Wootton 22 Resilience and presilience of protein network Authors: Brennan Klein, Mackenzie M Johnson, Ludvig Holmér, Keith Smith, Anshuman Swain, Laura Stolp, Douglas Reckamp, April S Kleppe 23 Resilient Life: An Exploration of Perturbed Autopoietic Patterns in Conway’s Game of Life Authors: Arta Cika, Elissa Cohen, Germán Kruszewski, Luther Seet, Patrick Steinmann, Wenqian Yin 24 Reviewing Early Warning Signals for Psychology: Theoretical and Practical Considerations Authors: Fabian Dablander, Anton Pichler, Arta Cika, Andrea Bacilieri 25 Semantic networks of simple agent-based models Authors: William Braasch, Pavel Chvykov, Levi Fussell, Xin Ran, Chiara Semenzin 26 Semantic Organization in the Color Distributions of Google Image Search Results Authors: Douglas Guilbeault, Ethan O Nadler, Mark Chu, Donald Ruggiero Lo Sardo, Aabir Abubaker Kar, Bhargav Srinivasa Desikan 27 Simulating Climate Change Belief Dynamics in Social Networks Authors: Ernest Aigner, Jackie Brown, Kyle Furlong, David Gier, Ludvig Holmér, Ritwika VPS 28 Simulating Dystopian Worlds: A Sci-Fi Agent-based Modeling Anthology Authors: Andrew Gillreath-Brown, Jeongki Lim, Harun Siljak 29 Taming the Complex via Concept Mapping Authors: Pam Mantri, Glory Dee Romo, Wenqian Yin 30 Too Much Information and Segregation Authors: Christopher Quarles, Wenqian Yin, Pablo Franco, Jordi Piñero, Brennan Klein 31 (A) Topologically Diverse Graph Ensemble Authors: Travis Moore, Anton Pichler, Xin Ran, Keith Smith, Yuka Suzuki 32 Understanding cell division dynamics using a neural network decision making model Authors: Kunaal Joshi, Anshuman Swain, Kazuya Horibe 33 Understanding leaf traits and ecology using novel complexity measures Authors: Levi Fussell, Emily Coco, Anshuman Swain 34 Urbanization and Malaria Incidences in Ghana: a spatio-temporal analysis Authors: Merveille Koissi Savia, Bhartendu Pandey, Anshuman Swain, Jeongki Lim 35 Using semantically-organized networks for the visualization of short texts Authors: Jeongki Lim, Christina Boyce-Jacino, Dakota Murray, John F Malloy, Ignacio Garnham, Pablo M Flores, Douglas Reckamp 36 What Can Social Complexity Teach Us about Cultural Evolution Authors: Alex Schaefer, Dries Daems, Ignacio Garnham, Kazuya Horibe 37

Many societal, environmental and technological challenges can be characterized as wicked problems by virtue of being difficult to understand, define and solve. examples include sustainable management and consumption of resources, resilient technical infrastructure or curbing plastic pollution of the oceans. One method of tackling such wicked problems is the use of computer-aided modelling and simulation. Model-based decision support is a growing discipline involving the use of computer models of complex systems to explore, understand and manage them. A core concept in model-based decision support is scenario discovery. In scenario discovery, a model’s inputs and outputs are related to understand under which conditions policy-relevant outputs may occur. In a first step, a diverse set of inputs is used to generate a variety of outputs. In a second step, the subset of decision-relevant outputs is identified among the outputs through some external criterion, such as a threshold value. Finally, the inputs which generated those outputs of interest are identified, and a generative rule set is induced which usefully predicts under which conditions an input will generate an output meeting the external criterion. This rule set bounds an input subspace of interest, from which (most of) the outputs of interest originate. While scenario discovery performs adequately for quasi-linear and simple models, it is not well suited to behaviorally complex, nonlinear models. This is both because external criteria are hard to define for complex model behaviors, and also because there are often significant interactions and dependencies between model inputs, which current rule induction algorithms have trouble identifying.......