Towards a Community Framework for Agent-Based Modelling

Agent-based modelling has become an increasingly important tool for scholars studying social and social-ecological systems, but there are no community standards on describing, implementing, testing and teaching these tools. This paper reports on the establishment of the Open Agent-Based Modelling Consortium, www.openabm.org, a community effort to foster the agent-based modelling development, communication, and dissemination for research, practice and education.Replication, Documentation Protocol, Software Development, Standardization, Test Beds, Education, Primitives

Assigning Creative Commons Licenses to Research Metadata: Issues and Cases

This paper discusses the problem of lack of clear licensing and transparency of usage terms and conditions for research metadata. Making research data connected, discoverable and reusable are the key enablers of the new data revolution in research. We discuss how the lack of transparency hinders discovery of research data and make it disconnected from the publication and other trusted research outcomes. In addition, we discuss the application of Creative Commons licenses for research metadata, and provide some examples of the applicability of this approach to internationally known data infrastructures.Comment: 9 pages. Submitted to the 29th International Conference on Legal Knowledge and Information Systems (JURIX 2016), Nice (France) 14-16 December 201

A model of the dynamics of organizational communication

We propose a model of the dynamics of organizational communication. Our model specifies the mechanics by which communication impact is fed back to communication inputs and closes the gap between sender and receiver of messages. We draw on language critique, a branch of language philosophy, and derive joint linguistic actions of interlocutors to explain the emergence and adaptation of communication on the group level. The model is framed by Te'eni's cognitive-affective model of organizational communication

Open semantic service networks

Online service marketplaces will soon be part of the economy to scale the provision of specialized multi-party services through automation and standardization. Current research, such as the *-USDL service description language family, is already defining the basic building blocks to model the next generation of business services. Nonetheless, the developments being made do not target to interconnect services via service relationships. Without the concept of relationship, marketplaces will be seen as mere functional silos containing service descriptions. Yet, in real economies, all services are related and connected. Therefore, to address this gap we introduce the concept of open semantic service network (OSSN), concerned with the establishment of rich relationships between services. These networks will provide valuable knowledge on the global service economy, which can be exploited for many socio-economic and scientific purposes such as service network analysis, management, and control

Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds. We explain how concepts from cognitive sciences could help AI to draw a roadmap towards human-like intelligence, with a focus on its social dimensions. As a first step, we propose to expand current research to a broader set of core social skills. To do this, we present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents using multiple grid-world environments featuring other (scripted) social agents. We then study the limits of a recent SOTA DRL approach when tested on SocialAI and discuss important next steps towards proficient social agents. Videos and code are available at https://sites.google.com/view/socialai.Comment: under review. This paper extends and generalizes work in arXiv:2104.1320

Fostering and Sustaining an Inclusive and Cognitively Diverse Learning Culture that Promotes Innovative and Agile Thinking

This Organizational Improvement Plan (OIP) addresses the problem of practice (PoP) of how leaders in the International School of Central Eastern Europe (a pseudonym) elementary school might foster and sustain a cognitively diverse and inclusive learning culture that promotes innovative and agile thinking. The plan is theory-based, research-informed, and looks to transfer learning into practice. It is built on a foundation of social constructivism and pragmatic-idealism and is firmly grounded in constructivist, ethical, and distributed leadership. Contextually oriented, the PoP grew in response to the need for schools to re-envision teaching and learning so students can thrive in an ever-changing, globalized society. It views change through continuous improvement. Bound by time constraints and the desire to empower faculty to lead change, an integrative approach was generated. The OIP intertwines an eight-step process with the organization’s inquiry cycle and key principles focused on maintaining a strengths-based approach to change. The framework connects the traditional hierarchy with a network structure. The dual operating system supports the cultivation of self-efficacy and collective efficacy fostering innovation in support of continuous change that focuses on improving teaching and learning. The resulting integrative framework, the accelerated improvement cycle, will be leveraged within the elementary division. The implementation of this approach will engage participants in reflective, reciprocal learning opportunities that encourage members to challenge one another’s assumptions to bring about positive, meaningful, and sustainable change. A critical examination of policy and the impact of the school’s dominantly Western philosophy and a workforce that does not personify the underrepresented minorities requires future investigation

Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.Comment: Forthcoming in Northwestern Journal of Technology and Intellectual Property, Volume 2