Agent-Based Modelling of Values: The Case of Value Sensitive Design for Refugee Logistics

Burken, Christine Boshuijzen-van; Gore, Ross; Dignum, Frank; Royakkers, Lmm Lambèr; Wozny, Phillip; Shults, F. LeRon

doi:10.18564/jasss.4411

Cited by 14 publications

(11 citation statements)

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“…Ambasciano also neglects the positive effects of our modeling approach, the results of which he fails to cite. MAAI models developed through our international collaborative efforts with humanities and social science scholars have aimed at discovering solutions to real-world challenges such as mitigating xenophobic anxiety and intergroup conflict , finding more progressive and culturally sensitive ways to solve immigration crises in Europe related to Refugee camps in Lesbos (Padilla, et al, 2018;Paloutzian, et al, 2021), analyzing the effectiveness of anti-childsex-trafficking policies (Alizada & Wildman, 2019), simulating processes that enhance minority integration in urban areas (Puga-Gonzalez, et al, 2019), addressing problems related to ethnocentrism (Lemos, et al, 2019), responding to global challenges related to the United Nations Sustainable Development Goals (Shults & Wildman, 2020b), reducing radicalization and violent extremism Ottman, et al 2022), uncovering ways to help change the Dutch immigration system to be more culturally compatible with Syrian Refugees (Boshuijzen-van Burken, et al, 2020), offering support for universities and other organizations struggling to set policies to slow the spread of COVID-19 (Wildman, et al, 2020), providing insight for therapeutic interventions for PTSD nightmares (McNamara, et al, 2021), illuminating the dynamics that promote prosocial attitudes and behaviors (Galen, et al, 2021), and preventing the spread of misinformation and anxiety in the wake of a pandemic (Antosz, et al, 2022). We welcome whatever assistance Ambasciano or other historians of religion, regardless of their previous participation in CSR 2.0, have to offer to help us address these critical social issues using the kinds of tools that policy makers are interested in engaging.…”

Section: Methodological and Epistemological Issuesmentioning

confidence: 99%

Paying the Piper: History, Humanities, and the Scientific Study of Religion

Lane¹,

Wildman

Shults

2022

Method Theory Study Relig.

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Here we respond to a recent article in this journal by Leonardo Ambasciano, in which he offers a high-level critique of “big data,” artificial intelligence, and computational approaches in the study of religion. The main thrust of his argument is that these approaches are fundamentally problematic both because of their negative effect on the humanities and because they inappropriately rely on “neoliberal philanthrocapitalist” funding. In our response, we refer to our experience working with computational scientists and humanities scholars in collaborative teams, where they stand shoulder to shoulder in equal collaboration with one another, each side relying on the distinctive value that the other provides as they attempt to create clearer and more valid descriptions, analyses, and explanations of complex human behaviors. We correct several errors of fact in Ambasciano’s article, focusing first on ideological and ethical issues and then on methodological and epistemological issues. We conclude by emphasizing several points on which we agree with his assessment.

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Section: Methodological and Epistemological Issuesmentioning

confidence: 99%

Paying the Piper: History, Humanities, and the Scientific Study of Religion

Lane¹,

Wildman

Shults

2022

Method Theory Study Relig.

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“…As Black (2001) noted in his history of refugee studies as a field, research on forced migration has been intimately linked with policy since its beginning. Voutira and Dona (2007) likewise conclude that "in refugee studies, scholarship is embedded in advocacy and advocacy in scholarship" (167), which captures not only the engagement with policy but also with practitioners.…”

Section: Mands and Predictive Assistancementioning

confidence: 99%

Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies

Frydenlund

2021

Journal on Migration and Human Security

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Modeling and Simulation (M&S) is a relatively unused research approach in forced migration studies. In most of its application areas, M&S is applied in several broad thematic policy-oriented topics: predicting human movement, humanitarian logistics, communicable diseases, healthcare, policing, and economics. More recently, there has been increased use of M&S in predicting human movement and health impacts resulting from climate change. Computer modeling has benefits for both policy and theoretical advancements in the field. M&S is often associated as a predictive tool that appeals to policymakers and planners. But M&S has many other significant benefits. For policymakers and planners, M&S provides a simulated environment to explore “what-if” scenarios. These scenarios can be adjusted to reflect different assumptions and conditions in the field. M&S can provide a means for translating interpretivist research findings—themselves rich in descriptions of actors, relationships, and dynamics—into visual artifacts that can communicate results to policymakers. It also can help to test the assumptions of theoretical findings, explore generalizability to other cases and conditions, and inform future data collection efforts. On the other hand, because models can take a long time to construct and test, they cannot provide rapid insights about evolving situations to inform decision-making at the onset of a new emergency. Data dashboards, the staple of modern humanitarian operations, are better for identifying emergent needs and tracking efforts. However, modeling what we know from one situation allows us to use those insights in systematic and strategic ways to improve operations for the future. M&S can also provide researchers with an additional tool that allows improved integration of multiple data types and sources. These data, assumptions, and theoretical constructs can be linked together in a complex, nonlinear, dynamic system that can then be set in motion to see if the simulated world reveals phenomena that are witnessed in the real world. But interdisciplinary communication and collaboration is hard and finding modelers who value interpretivist research can be even harder. M&S is not a silver bullet but provides a unique approach that incorporates dynamics and is becoming increasingly accessible to policymakers, practitioners, and academics, including those without computer programing experience. For policy, it is important to invest the time and resources to develop simulation models—from simple to complex—that help when planning for the evolution of events, or for the next event. Models of current refugee situations should inform where to concentrate efforts to advance durable solutions and how to plan for potential scenarios of return to countries of origin. Policymakers must recognize the long-term value of modeling what we know now to use later. Researchers of migration should become familiar with M&S paradigms so they can become part of the model design, development, and testing phases—at least part of the conversation. Better integration of migration scholars in design and development has the potential to strengthen models for policymaking, but perhaps more importantly, to strengthen the field by serving as an extra tool for testing theories, assumptions, and generalizability of findings to other locations and situations.

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“…Although, these architectures do not consider human values that may directly affect decision-making in different conditions. There are some works on value-reasoning models [24,25,34]. However, these works have not explored the integration challenges between planning, acting, goal-reasoning, and value-reasoning functions for human-centric smart environments.…”

Section: Literature Reviewmentioning

confidence: 99%

SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT

Rahimi¹,

Trentin²,

Ramparany³

et al. 2021

Preprint

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Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in humancentered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.

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Agent-Based Modelling of Values: The Case of Value Sensitive Design for Refugee Logistics

Cited by 14 publications

References 0 publications

Paying the Piper: History, Humanities, and the Scientific Study of Religion

Paying the Piper: History, Humanities, and the Scientific Study of Religion

Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies

SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT

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