Obligation Norm Identification in Agent Societies

Most works on norms have investigated how norms are regulated using institutional mechanisms. Very few works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. This paper describes a mechanism for identifying one type of norm, an obligation norm. The Obligation Norm Inference (ONI) algorithm described in this paper makes use of an association rule mining approach to identify obligation norms. Using agent based simulation of a virtual restaurant we demonstrate how an agent can identify the tipping norm. The experiments that we have conducted demonstrate that an agent in the system is able to add, remove and modify norms dynamically. An agent can also flexibly modify the parameters of the system based on whether it is successful in identifying a norm.Norms, Social Norms, Obligations, Norm Identification, Agent-Based Simulation, Simulation of Norms, Artificial Societies, Normative Multi-Agent Systems (NorMAS)

A Bayesian approach to norm identification

F. Meneguzzi thanks Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnologico (CNPq) through the Universal Grant (Grant ´ref. 482156/2013-9) and PQ fellowship (Grant ref. 306864/2013-4).Publisher PD

Tackling Energy Poverty with Artificial Intelligence: Challenges and Opportunities

Energy poverty occurs when households do not have access to, or cannot afford the energy services necessary to support their daily needs, including heating/cooling, washing, cooking, lighting and other activities. Energy poverty is a hidden, but important social problem because living in conditions without adequate energy access can lead to other health and social problems. Addressing energy poverty in a meaningful way requires first detecting households in precarious situations and then putting in place appropriate strategies to support them. In this research, we explore the potential for using artificial intelligence (AI) to model energy patterns that reflect situations of poverty. Using simulated energy consumption data in the New Zealand context, we show that AI, specifically machine learning models can achieve high predictive accuracy. We discuss challenges associated with using finer-grained approaches and opportunities for better prediction, prevention, and remediation of energy poverty

Norm violation in online communities -- A study of Stack Overflow comments

Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.Comment: 16 pages, 8 figures, 2 table

Towards offensive language detection and reduction in four Software Engineering communities

Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities

Improving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste

Big data are key building blocks for creating information value. However, information systems are increasingly plagued with useless, waste data that can impede their effective use and threaten sustainability objectives. Using a constructive design science approach, this work first, defines digital data waste. Then, it develops an ensemble artifact comprising two components. The first component comprises 13 machine learning models for detecting data waste. Applying these to 35,576 online reviews in two domains reveals data waste of 1.9% for restaurant reviews compared to 35.8% for app reviews. Machine learning can accurately identify 83% to 99.8% of data waste; deep learning models are particularly promising, with accuracy ranging from 96.4% to 99.8%. The second component comprises a sustainability cost calculator to quantify the social, economic, and environmental benefits of reducing data waste. Eliminating 5948 useless reviews in the sample would result in saving 6.9 person hours, $2.93 in server, middleware and client costs, and 9.52 kg of carbon emissions. Extrapolating these results to reviews on the internet shows substantially greater savings. This work contributes to design knowledge relating to sustainable information systems by highlighting the new class of problem of data waste and by designing approaches for addressing this problem

Barriers for Social Inclusion in Online Software Engineering Communities -- A Study of Offensive Language Use in Gitter Projects

Social inclusion is a fundamental feature of thriving societies. This paper first investigates barriers for social inclusion in online Software Engineering (SE) communities, by identifying a set of 11 attributes and organising them as a taxonomy. Second, by applying the taxonomy and analysing language used in the comments posted by members in 189 Gitter projects (with > 3 million comments), it presents the evidence for the social exclusion problem. It employs a keyword-based search approach for this purpose. Third, it presents a framework for improving social inclusion in SE communities.Comment: 6 pages, 5 figures, this paper has been accepted to the short paper track of EASE 2023 conference (see https://conf.researchr.org/track/ease-2023/ease-2023-short-papers-and-posters#event-overview

A Comparative Study on Apprenticeship Systems Using Agent-Based Simulation

In this paper, we investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. Apprenticeship is one of the major means to transfer skills in a society. We consider five societies: the Old Britain system (AD 1300s−1600s), the British East India Company (AD 1600s − 1800s), Armenian merchants of New-Julfa (AD 1600s − 1700s), contemporary German apprenticeship (1990s), and the “Modern Apprenticeship” in Britain (2001). In comparing these systems, using an agent-based simulation model, we identified six characteristics which impact the success of an apprenticeship programme in a society, which we measured by considering three parameters, namely the number of skilled agents produced by the apprenticeships, programme completion, and the contribution of programmes to the Gross Domestic Income (GDI) of the society. We investigate different definitions for success of an apprenticeship and some hypothetical societies to test some common beliefs about apprenticeships' performance. The simulations suggest that a) it is better to invest in a public educational system rather than subsidising private contractors to train apprentices, b) having a higher completion ratio for apprenticeship programme does not necessarily result in a higher contribution in the GDI, and c) governors (e.g. mayors or government) that face significant emigration should also consider employing policies that persuade apprentices to complete their programme and stay in the society after completion to improve apprenticeship efficacy.publishedVersio