Marco A. Janssen - US grants

Many societies have endured for long periods, successfully coping with uncertainty, disturbance, and change in the environment. Many other societies have failed in this regard. The core question addressed in this interdisciplinary research project is why some social-ecological systems are more successful in dealing with disturbances and change in the environment than others. The investigators, who come from a broad range of composed of social, physical, and engineering science fields, hypothesize that an important factor bearing on this question is a well-known phenomenon in engineering: a system cannot be robust to all classes of disturbances. Thus, in developing mechanisms to address an existing suite of uncertainties and environmental risks (becoming robust to a particular class of disturbances), society necessarily becomes vulnerable to other classes of disturbances. Through the application of several complementary quantitative techniques to a suite of mathematical models based on a large set of case studies, the research team will explore such robustness-fragility trade-offs in a range of simple irrigation societies. The research team will employ methods and insights from applied mathematics, electrical engineering (control), resource economics, archaeology, and ecology to develop an integrated approach to study how societies deal with uncertainty, disturbance, and change. The products from the project will be (1) an empirical database and a suite of formal mathematical models relating to the basic biophysical, social, and institutional characteristics of several irrigation societies, (2) a deeper understanding of how societies may become fragile as they attempt to cope with uncertainty and change in the environment, and (3) new tools for resource managers: a set of design principles for robust institutions that perform well in the face of both social and environmental disturbances.

This project will yield fundamental new knowledge concerning the interaction between human social dynamics and environmental change. It also will improve resource management practice and to enhance mathematical education. By bringing together techniques from a range of disciplines, the investigators will develop new tools for the study of social-ecological systems that are able to cope with more complexity than traditional approaches. The research results have the potential to improve resource management practices by providing tools to help predict when alternative responses to disturbance and environmental change will succeed and how they will most likely fail. Research findings will be translated into practice through the efforts of the Resilience Alliance, an international organization that produces research, educational materials, and practical tools for resource managers. The research project will contribute to ongoing activities at Arizona State University designed to enhance mathematical literacy in the life and social sciences by engaging both graduate and undergraduate students in a problem-focused, interdisciplinary research program involving mathematics, economics, engineering, ecology, and archeology. An award resulting from the FY 2005 NSF-wide competition on Human and Social Dynamics (HSD) supports this project. All NSF directorates and offices are involved in the coordinated management of the HSD competition and the portfolio of HSD awards.

The Dynamics of Rules in Commons Dilemmas

Various social sciences have contributed to understanding how humans make decisions in a given rule set of experimental games, such as social dilemmas, coordination, and bargaining. However, the rules of the games are not fixed in real-life settings. No systematic studies have been performed on the question of how humans are able to change the rules in commons dilemmas. From field studies it is well known that people invest significant effort in crafting new rules. This project will study what causes individuals to invest in rule development, and which cognitive processes explain the ability of humans to craft new rules.
The main research plan consists of three components: laboratory experiments, experiments in the field, and agent-based models. A series of laboratory experiments will be conducted to analyze the effect of the option to change the rules of the game on the level of cooperation and the consequences of communication and options to migrate between groups. The processes by which individuals learn as they try to optimize the rule set for a specific game will be explored. The field experiments are aimed to study the role of experience in the ability to craft rules. A number of communities in Colombia and Southeast Asia that have a dominant resource use of fisheries, forest, or irrigation will participate. In these role games, groups of resource users will be asked to craft rules for a hypothetical common-pool resource, resembling the key problems of fisheries, forests, or irrigation. The capacity of the groups to craft rules for resources they are familiar with, and some with which they are not familiar will be investigated.
The agent-based models will be based on the experimental data and will explore the mathematical properties of co-evolution of strategies and rules. These models will focus on identifying components of the learning processes, information exchange, and structure of the game that affect the ability of the system to co-evolve to a cooperative equilibrium. Based on the experiments in the laboratory and the field, a number of web-based educational tools will be developed, to allow students to experiment with rule-crafting in commons dilemmas.
This project will contribute to the methodological development of agent-based models by combining laboratory and field experiments and to the empirical testing of alternative behavioral models. It may have a broad impact in political science and ecosystem governance by deriving an understanding of what factors affect the ability of resource users to change institutional rules effectively. The project will strengthen the collaboration on experimental research and agent-based modeling between Asia, Latin American and the USA. The web-based educational tools developed will be freely available to students and instructors all over the world.
This project is supported by an award resulting from the FY 2004 NSF-wide competition on Human and Social Dynamics (HSD). The Directorate for Computer and Information Sciences and Engineering and the Office of International Science and Engineering are also contributing to support of this project. Coordinated management of the HSD competition and the portfolio of HSD awards involves all NSF directorates and offices.

This project establishes a consortium in the social and natural sciences for facilitating agent based modeling (ABM) of socioecological dynamics. This organization will serve the scientific community as a framework for collaboration and interdisciplinary research, emphasizing the complex interactions between humans and the environment. A workshop, to be held in early 2007 will bring together leading and innovative practitioners of ABM research in social and natural sciences to organize the consortium, using successful examples of community frameworks for cybertool development in other research domains. Invited participants span a wide range of scientists employing ABM in socio-ecological research and ABM platform developers. The workshop will be followed by a pilot project to develop and evaluate a suite of resources that will help consortium members -- and practitioners in the social and natural sciences more broadly -- make more effective use of advanced ABM simulation protocols in ongoing and planned research. These resources include an archive of agent based models and library of ABM components that can be used by researchers to initiate new modeling efforts and assist peer-review of publications of research involving ABM; a server for collaborative development of better ABM interfaces and cybertools to improve the usability and usefulness of ABM for socioecological research (e.g., concurrent version server, or cvs); and a testbed of standard data for developing model evaluation protocols. The consortium also will promote a community-wide set of best practices for model dissemination and frameworks for model interchange, and initiate a training program in ABM aimed at social and natural scientists.

The initial organizing workshop and pilot project are a collaboration between the School of Human Evolution & Social Change (Arizona State University), the Resilience and Adaptive Management Group (University of Alaska), the Department of Anthropology (University of Arizona), the Center for Social Dynamics and Complexity ((Arizona State University), and the Santa Fe Institute. The workshop will take place at the Center for Social Dynamics and Complexity, and the pilot project resources server will be managed there.

The interactions between humans and their environments are both dynamic and complex. This complexity has intersected over the past century with increasingly rapid population growth, urbanization, and technological development to make human society an important driver of environmental change. This has created a rate and degree of social-environmental change that threatens to exceed our abilities to adapt using traditional strategies. Hence, it is imperative that humans find better ways to understand these global socio-ecological systems (or "socioecosystems"), and anticipate their social and natural consequences in order to lessen the risk of potentially severe socio-natural catastrophes. Recent advances in information technology offer powerful new tools to assist in understanding and managing these coupled social and natural systems. In this context, agent based models have recently emerged as a promising cybertool to study the dynamics of complex human and biological systems, integrating individual perceptions and behaviors in the contexts behavioral ecology, game theory of decision-making, and geospatial representations of the world. The consortium created through this project will establish a community-wide framework to promote more effective use of agent based modeling in socioecological research, and make its results accessible and useful to diverse audiences. Since questions about the dynamics of socio-ecological systems lie at the heart of increasingly critical policy debates at global scale, the long-term agenda of the program initiated here also offers the potential to better inform discussions of environmental policy and its consequences on human society.

Management of shared or ?common pool? resources -- like shared grazing or fishing grounds or the fragile reputation of an industry or profession -- is a hard problem. The possibility that some parties will over consume, pollute or otherwise damage the resource as they extract their personal value from it is always present. To date, researchers have identified basic principles for how humans organize themselves to govern common resources, but these are mostly static principles. This project seeks aims to develop a next-generation understanding: what makes groups successful in changing the institutional rules that govern behavior related to common resources?

Group experiments and agent-based modeling techniques will be used to test how people create formal rules as they interact and how and why rule sets evolve. The spatially explicit real-time experimental environment will allow researchers to test the crafting of formal rules for diverse types of ecologies. Data from experiments are used to develop agent-based models to examine institutional evolution and adaptation in a wider set of typologies of ecologies and constitutional arrangements.

As part of this CAREER project, simulated, visually compelling, common pool environments will be created for subjects to use during experiments. These simulations will be adapted for educational use. They will enable students to learn more about common pools, to learn relevant computations, and to learn to craft governance mechanisms in complex social situations. An e-book on social simulation will also be developed, to help other scholars develop useful materials.

This project establishes a scientific research collaboration network to support and expand the development and use of computational modeling in the social and life sciences. The COMSES (COmputational Modeling for SocioEcological Science) network aims to be broadly inclusive of social and natural scientists using (and desiring to use) advanced modeling to study coupled natural and human, or 'socioecological systems' (SES).

The often non-linear, non-intuitive, and even surprising relationships and consequences that emerge from complex interactions between human social practice and the biophysical environment make it imperative that scientific models - including their underlying assumptions, algorithmic processes, logical consistency, and connections with empirical data - be transparent and quantitative. For this reason, computational modeling in general and agent-based modeling (ABM) in particular are important tools for research in socioecological sciences. Yet ABM remains little used or understood in the broader scientific communities involved in the science of coupled natural and human systems. Rather, it is widely perceived as inaccessible to social and natural scientists in spite of the fact that ABM has existed as a potential research tool for nearly two decades.

The primary goal of the COMSES network is to serve as a coordinated community of practice and a conduit to expedite knowledge exchange for computational modeling in SES. The network will build on the NSF-funded OpenABM Consortium http://www.openabm.org. It will seek to grow scientific infrastructure so as to better serve research that uses computational modeling by:

- establishing an interactive, online archive for computational models and providing standard tools and data for model evaluation;
- promoting a common standard for describing models;
- developing educational curricula for embedding modeling and computational thinking in the interdisciplinary study of social and natural sciences.
- establishing a new e-journal dedicated to publishing research in the social and natural sciences that involves computational modeling, and studies of modeling methods and empirical evaluation.

Through the development of exemplar curricula and training opportunities, the project will endeavor to stimulate new interdisciplinary learning opportunities for students at multiple levels who represent the next generation of scientists. With the ever-increasing importance of information technology and related cybertools in today's world, training in advanced modeling techniques and their application in the social sciences will broadly benefit students' careers.

Many of the environmental challenges that we face in the 21st century may also be considered social problems because they affect our quality of life and can only be addressed by human actions. The COMSES network has the potential to transform the social and natural sciences in profound ways by developing computational tools and expertise that will help us to gain a much deeper understanding of the relationships between humans and their environment, and allow us to better predict the likely effects of future policies and decisions.

Long-lived coupled natural human systems (CNHs) are often distinguished by how they have evolved the right fit between their biophysical and social sub-systems. Researchers have characterized this fit in terms of the close feedbacks that enable a system to function well when faced with a known set of disturbance regimes. This project addresses a key question that naturally arises when these systems are exposed to a new set of disturbance regimes or novel change as is likely to occur with increased globalization and climate change: to what extent do the interdependencies that developed to strengthen the system's capacity to fit to a certain set of disturbances limit or enhance its capacity to refit to new conditions? To address this question, the project team will focus on a particular class of CNHs - small scale irrigation systems where the built infrastructure plays a critical role in resource provision. A multi-method approach will be employed that begins with the qualitative analysis of a large number of case studies to develop a typology of irrigation systems. A subset of previously examined exemplar cases will be revisited to examine how they have coped with new vulnerabilities that have emerged. In parallel, field experiments will be conducted with resource users and infrastructure providers that apply principles of behavioral economics to social dilemmas that emerge in irrigation systems in order to test the robustness of collective action to different stressors. These will be followed by laboratory experiments with college students to examine whether the findings can be generalized to other systems. Finally, insights from the case studies and experiments will be used to develop formal dynamic models of prototypical irrigation-based CNHs. Using tools from dynamical systems and robust control complemented by numerical experiments and simulation (including agent-based approaches), the qualitative dynamics and associated robustness-vulnerability characteristics of these systems will be studied in order to distill a set of institutional design principles for use in the development of tools for anticipatory governance.

In addition to the study of coupled natural human systems (CNHs) in its own right, this project is motivated by an important practical consideration. The pace at which social and natural systems are currently changing - driven to a large degree by globalization and climate change - has challenged the capacity of several CNHs to self-organize and respond effectively to this change. Ongoing field studies have shown that small scale farms (less than 2 hectares), which constitute 90% of farms worldwide and consume an estimated 70% of developed water supplies, are the most vulnerable to such changes. Since these farms also produce 40% of agricultural output globally and support the majority of world?s poorest people, it is essential to enhance their adaptive capacity in order to maintain food security and alleviate poverty. Focusing on the in-depth study of small-scale irrigation systems, this project pushes ideas of resource governance beyond traditional approaches. Rather than seeking the "best" policies for a specific system under a given set of shocks, it seeks to understand how CNHs cope with change under a wide range of disturbance regimes. The theoretical foundation for the project is based on a key insight from engineering: feedback (of which management and governance in CNHs are examples) can be used to stabilize a system and reduce its sensitivity to a given set of disturbances. However, this stabilizing feedback can increase sensitivity to new types of disturbances. By studying responses to a suite of disturbances, this project helps advance a more nuanced understanding of evolution of CNHs in terms of recognizing how in becoming finely-tuned to certain disturbances, systems become increasingly vulnerable to others. Building on this insight through theoretical modelling as well as case studies and field experiments from a wide variety of settings in the southwestern US, India, Nepal and Thailand, this project will develop a set of tools that will contribute to the capacity of policy makers to better anticipate emerging vulnerabilities and design learning and adaptive mechanisms to cope more effectively with change.

There is growing interest in the use of information and communication technologies for community engagement and for crowd-sourcing solutions to difficult problems through challenges and prizes. Governmental and nongovernmental organizations are being encouraged to design, deploy, manage and support appropriate online platforms to address both goals and improve economic competitiveness. These governance challenge platforms can create novel pathways for citizen participation, increase openness of governance activities, and increase both the effectiveness and legitimacy of the governing organization. Arizona State University is developing a new University-wide challenge platform to enhance community engagement and to solicit ideas from its 50,000-member University community to solve eight broad challenges, but little is known about the design, use and effects of such platforms.

The research team will engage the platform design team to incorporate the affordances required to improve the overall user experience and to test applicable theories of team composition, governance structures, legitimacy, and team capacity and commitment. Once these features have been developed, they will be used in a series of field studies designed to identify theoretical extensions and potential boundary conditions in online community engagement. The studies will initially map community participation, trace how participation spreads through the community, and test the effects of real-time feedback on the community?s participation patterns. The next phase will explore the impact of voting mechanisms on community dynamics, on perceptions of governance accountability, and on more sophisticated forms of community involvement. Finally, relationships between team formation, structure, diversity and effectiveness will be investigated focusing on the quality of the solutions generated.

Many of the challenges facing contemporary society, such as emission reductions or vaccination for infectious diseases, are collective action problems. To address these challenges, new approaches are needed to understand, stimulate and sustain collective action in large heterogeneous populations. To promote cooperative behavior at large scales, this project will develop computational tools to facilitate the context for cooperation - homogeneity, effective communication - observed in smaller scale case studies and field experiments. The investigators will test new ways to increase collective action using mobile applications and social media.

The project will use controlled decision-making experiments to test whether contributions to collective action can be increased by providing the right messages to the right people, such that cooperative behavior can cascade through a social network. Empirical research has shown that individuals are more likely to participate in collective action if they expect others like them to participate. Experiments will be performed in online social networks of students at Arizona State University to test the proposed approach to actualizing collective action within the university community. Data from the experiments will be used to develop mathematical models of collective action in social networks.

Broader impacts: The research will lead to new theoretical frameworks for understanding collective action and provide concrete tools to apply these insights. Insights about what kind of feedback to whom is most effective for increasing collective action may make social media a potential effective policy tool for organizations. Potentially, the project will have societal impact by promoting collective action in important areas such as healthcare, voter participation, and energy conservation.

Norms of transparency and knowledge sharing in science encourage new research to build on prior discoveries, leading to rapid innovation and significant societal returns. This project establishes a new activity to help make scientific computation and data science more transparent and accessible. Computation has evolved from tools for assisting scientific research to digital laboratories where fundamental scientific discoveries take place. This is increasingly so for social and ecological sciences, which are combining big data with computational models to better understand the social and earth systems whose complex dynamics underlie many of the grand challenges faced by humanity today. Organized as a Spoke in the National Science Foundation's Big Data Innovation Hub and Spoke network, this project establishes a next generation, online Computational Model Library (CMLX).

In the CMLX, software code for computational models used in social and ecological sciences is published and freely accessible to the scientific community and general public, and linked with an online database of scientific papers reporting on associated model-based science. In addition to the code library, the CMLX is developing an online repository to archive end-to-end workflows of model-based science, where the entire research process can be followed and reproduced, from data synthesis, to modeling, analysis, and visualization. A regional Working Group - including software developers, data providers, and user communities - will provide advice and expertise, as well as help establish community-wide standards for transparency and accessibility in scientific computation. The CMLX is enabling transparent access to scientific procedures, models, and data that can be used to assess the consequences of alternative scenarios, policies, and assumptions for more sustainable management of complexly coupled human and earth systems.

This project is designed to support and advance next generation, interdisciplinary science of the complexly interacting societal and natural processes that are critical to human life and well-being. Computational models are powerful scientific tools for understanding these coupled social-natural systems and forecasting their future conditions for evidenced-based planning and policy-making. This project is led by the Network for Computational Modeling in Social and Ecological Sciences (CoMSES.Net). CoMSES.Net's science gateway promotes knowledge sharing among scientists and with the general public, and enables open, online access to sophisticated computational models of social and ecological systems. CoMSES.Net's partners in this project (the Community Surface Dynamics Modeling System and Consortium of Universities for the Advancement of Hydrologic Science) also enable knowledge sharing and provide open, online repositories of models in the earth sciences. This project will enhance these science gateways and create online educational materials to make these critical technologies easier to find, understand, and use for scientists and non-scientists alike. By integrating innovative technology with training and incentives to engage in best practice standards, this project will stimulate innovation and diversity in modeling science. It will enable researchers to build on each other's work and combine it in new ways to address societal and environmental challenges. The cybertools and educational programs developed in the project will be openly accessible not just to research institutions but also to smaller colleges, state and local governments, and a broader audience beyond the science community. The project will give decision-makers and the data scientists who support them access to a larger and more varied toolkit with which to explore potential solutions to societal and environmental policy issues. A long-term aim of the project is to support an evolving ecosystem of diverse, reusable, and combinable models that are transparently accessible to anyone in the world. Sustainable planetary care and management is a challenge that confronts all of humanity, and requires knowledge, histories, methods, perspectives, and engagement of researchers, decision-makers, and private citizens across the country and throughout the world.

The project will develop an Integrative Cyberinfrastructure Framework (ICF) to enable innovative next-generation modeling of human and natural systems, and build capacity in modeling science. It will support a set of activities that integrate the human and technological components of cyberinfrastructure. 1) Software tools will be developed that augment model codebases with modern software development scaffolding to facilitate reuse, integration, and validation of model code. 2) The project will provide high-throughput computing (HTC) resources for simultaneously running numerous iterations of models needed to capture stochastic variability, explore a parameter space, and generate alternative scenarios; 3) Online training activities will build expertise and capacity to make effective use of the cybertools and the HTC resources; 4) The ICF will engage a global modeling science community to provide professional incentives that encourage researchers to adopt best practices and catalyze innovative science. Leveraging existing NSF investments, the ICF will be developed and deployed by the Network for Computational Modeling in Social and Ecological Sciences (CoMSES.Net), in partnership with the Community Surface Dynamics Modeling System (CSDMS), Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI), Open Science Grid, Big Data Hub/Spoke network, and Science Gateways Community Institute. Computational models have emerged as powerful scientific tools for understanding coupled social-biogeophysical systems and generating forecasts about future conditions under a range of climate, biogeophysical, and socioeconomic conditions. CoMSES.Net, CSDMS, and CUASI are scientific networks, with online science gateways and code archives that enable open access to computational models for an international community of social, ecological, environmental, and geophysical scientists. However, the full value of accessible, well-documented models only can be realized if their code is also widely reproducible and reusable, with a potential for integration with other models. In order to confront critical challenges for understanding the coupled human and natural systems of today's world, modeling scientists also need HTC environments for upscaling models and exploring high-dimensional parameter spaces inherent in representing these systems. The ICF is designed to meet these challenges. By integrating technology with intellectual capacity-building, the ICF will stimulate innovation and diversity in modeling science by letting creative researchers build on each other's work more readily and combine it in new ways to address societal-environmental challenges we have not yet perceived. The tools and training resources will be openly accessible not just to leading research institutions but also to the many smaller colleges, state and local governments, and a broader audience beyond science. They will provide decision-makers and the data scientists who support them access to a much larger and more varied toolkit with which to explore potential solution spaces to social and environmental policy issues. The proposed ICF is also designed to help transform scientific modeling practice, including incentives that can help early career researchers shift from creating models to solve problems specific to a particular project to models that are also useful for others. The project will help support a future evolving ecosystem of diverse, reusable, and integrable models that are transparently accessible to the broader community.

This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering, with the Division of Social and Economic Sciences in the Directorate for Social, Behavioral & Economic Sciences also contributing funds.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project explores the spread, use, and impact of imprecise language (such as buzzwords) on collective action and decision-making, with a specific focus on environmental conservation issues. When navigating these complex issues, individuals and organizations must make decisions and communicate about how priorities are defined, how resources are allocated, what actions are to be taken, and with whom to cooperate. Not enough is known about the role that imprecise language and buzzwords play in this decision-making process. They may lead to confusion by creating ambiguity, but they may also bring diverse stakeholders together by increasing trust or a shared sense of purpose. Because there are limited resources available for the increasingly urgent issues of environmental conservation and a transition to a just climate future, this research serves an important role in informing how language may influence the allocation of such scarce resources.<br/><br/><br/>Through a multi-study, mixed methods approach, this project examines the use of one type of imprecise language – buzzwords – within the context of environmental conservation as a case study. It examines publications and engages with actors from academia, non-governmental organizations, international environmental conventions, and mainstream news media focused on conservation. Utilizing a computational text analysis, a series of interviews, and an experimental survey, this project seeks to: (1) develop and deploy a novel definition of buzzwords, (2) identify a list of buzzwords used across the conservation sector, (3) build an expert mental model of imprecise language and buzzwords, and (4) empirically test the impact of buzzwords on decision-making. This research meets a growing need to further our understanding of how language influences decision-making in contexts characterized by urgency and uncertainty, while providing useful information to the conservation community.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.