AEINS: Interactive Narrative Role in Fostering Character Education

Promoting ethical, responsible, and caring young people is a perennial aim of education. Efforts have been done to find other teaching ways other than traditional ones such as games and role play. Narrative-based computer games have found their way as engaging learning platforms that allow collaboration of humans and computers in the creation of innovative experiences. In this paper, we focus on the design of an adaptive, interactive narrative model that makes use of a student model to provide an individualized story-path and an individualized learning process. In other words, we aim to have strong learning objectives underpinned by effective story telling. The adaptive narrative model has been deployed in the educational game environment, AEINS, along with the use of the Socratic Method and pedagogical agents to help teaching in the ethics domain. Evaluation results indicate the usefulness of the design and provide evidence on the development of moral reasoning and the transfer of moral virtues to its users

Mesoscopic effects in an agent-based bargaining model in regular lattices

The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the multiagent bargaining model by Axtell, Epstein and Young [1] modifying the assumption of global interaction. Each agent is endowed with a memory and plays the best reply against the opponent’s most frequent demand. We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can lead to new persistent regimes different from those found in previous research. In particular, community structure in the intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the hindering diffusion effect of fluctuating agents at their borders.Spanish Ministry of Science and Innovation, references TIN2008-06464-C03-02 and CSD2010-00034 (CONSOLIDER-INGENIO 2010), and by the Junta de Castilla y Leon, references VA006A009, BU034A08 and GREX251-200

The Impact of Agent-Based Models in the Social Sciences after 15 Years of Incursions

This paper provides an overview on the impact of agent-based models in the social sciences. It focuses on the reasons why agent-based models are seen as important innovations in the recent decades. It is aimed to evaluate the impact of this innovation on various disciplines, such as economics, sociology, anthropology, and behavioural sciences. It discusses the advances it contributed to achieve and illustrates some comparatively new fields to which it gave rise. Finally, it emphasizes some research issues that need to be addressed in the future

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions