Existing courses on agent-based modeling and simulating (ABMS) are mainly aimed at doctoral students and many modelers have acquired their ABMS skills by teaching themselves. This paper reports and reflects on the development of an undergraduate course on ABMS of social systems. It presents a problem-based approach to teaching ABMS of social systems, the Integrated Learning Outcomes (ILOs), and the course structure. This paper discusses the constructive alignment of the syllabus, presents the results from the course evaluation, and draws conclusions for further editions of the course. Rather than proposing how such courses should be structured, we discuss the feasibility of the pursued research-based learning approach. Our goal is to inspire other researchers and teachers to develop similar courses, to encourage the establishment of a general curriculum, and to promote ABMS in undergraduate education.

Agent-Based Social Simulation (ABSS), i.e., the study of social systems through Agent-Based Modeling, has great potential in serving as policy support. However, challenges in the modeling for policymaking process prevent this potential to be fully realized. For instance, the models might not be deemed sufficiently credible or appealing to decisionmakers.This licentiate thesis aims to contribute to the understand of these challenges, and to identify challenges and opportunities in how to increase the credibility of ABSS models in policymaking.

First, we investigate the modeling of human behavior and what challenges there are that may affect models’ suitability for policymaking. Properly representing individuals’ decision-making can serve to increase model accuracy, model descriptiveness and recognizability of the modeled system, which in turn can increase model credibility. Taking models of the COVID-19 pandemic as an example, we performed reviews that analyzed what aspects of human behavior were modeled, and how these aspects relate to what conclusions can be drawn from the model. The studies found that many of the aspects that seem relevant were rarely included in the studied models. Three challenges were identified with regards to being able to build more descriptive behavior models within the time constraints posed by decision-makers: improvements to modeling tools and software, model reuse, and data availability.

Second, we look at the verification and validation (V&V) of ABSS models. The proper evaluation of models is crucial for model credibility to be ensured. In the study of COVID-19 models, V&V activities were found to be rarely documented (if performed at all). To this end, we suggest the continuous use of a ”V&V plan” during the modeling process,and proposes an accreditation framework for institutions to be able to perform external validation.

Third, we consider the combination of models and model results. Model combination can lead to more credible models as it allows modelers to use well-established, well validated models to represent parts of a system, while combined simulation results often are more robust than those of a single model. A literature survey was performed, identifying six different types of combinations: Ensemble Modeling, Meta Analysis, Model Merging, Models as Modules, Model Integration and Model Chaining. For each type, we identified purpose and examples, as well as existing challenges for the combination of both simulation models in general and ABSS models in particular.

Proper modeling of human behavior is crucial when developing agent-based models to investigate the effects of policies, such as the potential consequences of interventions during a pandemic. It is, however, unclear, how sophisticated behavior models need to be for being considered suitable to support policy making. The goal of this paper is to identify recommendations on how human behavior should be modeled in Agent-Based Social Simulation (ABSS) as well as to investigate to what extent these recommendations are actually followed by models explicitly developed for policy making. By analyzing the literature, we identify seven relevant aspects of human behavior for consideration in ABSS. Based on these aspects, we review how human behavior is modeled in ABSS of COVID-19 interventions, in order to investigate the capabilities and limitations of these models to provide policy advice. We focus on models that were published within six months of the start of the pandemic as this is when policy makers needed the support provided by ABSS the most. It was found that most models did not include the majority of the identified relevant aspects, in particular norm compliance, agent deliberation, and interventions’ affective effects on individuals. We argue that ABSS models need a higher level of descriptiveness than what is present in most of the studied early COVID-19 models to support policymaker decisions. 

This paper explores the topic of model credibility of Agent-based Models and how they should be evaluated prior to application in policy-making. Specifically, this involves analyzing bordering literature from different fields to: (1) establish a definition of model credibility -- a measure of confidence in the model's inferential capability -- and to (2) assess how model credibility can be strengthened through Verification, Validation, and Accreditation (VV&A) prior to application, as well as through post-application evaluation. Several studies have highlighted severe shortcomings in how V&V of Agent-based Models is performed and documented, and few public administrations have an established process for model accreditation. To address the first issue, we examine the literature on model V&V and, based on this review, introduce and outline the usage of a V&V plan. To address the second issue, we take inspiration from a practical use case of model accreditation applied by a government institution to propose a framework for the accreditation of ABMs for policy-making. The paper concludes with a discussion of the risks associated with improper assessments of model credibility. 

This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.

The increasing popularity of e-commerce requires efficient solutions for the provision of last mile logistics. There are different approaches for delivering parcels, e.g., home delivery, service points, or parcel lockers, which have different advantages and disadvantages for customers and logistics providers in terms of flexibility, accessibility, and operating costs. We have studied a novel transportation solution where electric vehicles dynamically set up smart cargo boxes, from which customers can fetch their delivery at any time of the day. This provides customers with a more flexible access to their packages and allows the service provider to deliver the parcels more efficiently. In this article, we present the results of a feasibility study conducted in Västra Hamnen, Malmö (Sweden). The developed simulation model shows that smart boxes not only are a viable approach for efficient last mile deliveries, but also result in considerably smaller travel distances compared to conventional package delivery system.

The increasing popularity of e-commerce requires efficient solutions for the provision of last mile logistics. There are different approaches for delivering parcels, e.g., home delivery, service points, or parcel lockers, which have different advantages and disadvantages for customers and logistics providers in terms of flexibility, accessibility, and operating costs. We have studied a novel transportation solution where electric vehicles dynamically set up smart cargo boxes, from which customers can fetch their delivery at any time of the day. This provides customers with a more flexible access to their packages and allows the service provider to deliver the parcels more efficiently. In this article, we present the results of a feasibility study conducted in Västra Hamnen, Malmö (Sweden). The developed simulation model shows that smart boxes not only are a viable approach for efficient last mile deliveries, but also result in considerably smaller travel distances compared to conventional package delivery.

When planning interventions to limit the spread of Covid-19, the current state of knowledge about the disease and specific characteristics of the population need to be considered. Simulations can facilitate policy making as they take prevailing circumstances into account. Moreover, they allow for the investigation of the potential effects of different interventions using an artificial population. Agent-based Social Simulation (ABSS) is argued to be particularly useful as it can capture the behavior of and interactions between individuals. We performed a systematic literature reviewand identified 126 articles that describe ABSS of Covid-19 transmission processes. Our reviewshowed that ABSS is widely used for investigating the spread of Covid-19. Existing models are very heterogeneous with respect to their purpose, the number of simulated individuals, and the modeled geographical region, as well as how they model transmission dynamics, disease states, human behavior, and interventions. To this end, a discrepancy can be identified between the needs of policy makers and what is implemented by the simulation models. This also includes how thoroughly the models consider and represent the real world, e.g. in terms of factors that affect the transmission probability or how humans make decisions. Shortcomingswere also identified in the transparency of the presented models, e.g. in terms of documentation or availability, as well as in their validation, which might limit their suitability for supporting decision-making processes. We discuss how these issues can be mitigated to further establish ABSS as a powerful tool for crisis management.