Amineh Ghorbani

MAIA: a Framework for Developing Agent-Based Social Simulations

In this paper we introduce and motivate a conceptualization framework for agent-based social simulation, MAIA: Modelling Agent systems based on Institutional Analysis. The MAIA framework is based on Ostroms Institutional Analysis and Development framework, and provides an extensive set of modelling concepts that is rich enough to capture a large range of complex social phenomena. Developing advanced agent-based models requires substantial experience and knowledge of software development knowledge and skills. MAIA has been developed to help modellers who are unfamiliar with software development to conceptualize and implement agent-based models. It provides the foundation for a conceptualization procedure that guides modellers to adequately capture, analyse, and understand the domain of application, and helps them report explicitly on the motivations behind modelling choices. A web-based application supports conceptualization with MAIA, and outputs an XML file which is used to generate Java code for an executable simulation.

A method for developing agent-based models of socio-technical systems

Agent-based modeling is one of the popular tools for analyzing complex socio-technical systems. Because of the complex nature of such systems a systematic methodology is required to guide the modeling process. By studying the existing methodologies in MAS we distinguished four major differences between MAS and ABM regarding goals, system scale and diversity, level of system understanding and verification and validation concerns. In this paper we take these differences into account and based on more than 25 case studies, we present a methodological framework for developing agent-based models that consists of five general iterative phases. These phases namely: system analysis, model design, detailed design, implementation and evaluation further consists of smaller step that are also addressed in this paper. This methodology provides a tool independent template while respecting the specific requirements for ABM.

The Application of the Locally Linear Model Tree on Customer Churn Prediction

Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Thus many prediction models are presented to detect churning customers. The objective of this paper was to improve the predictive accuracy and interpretability of churn detection. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Applied to the data of a major telecommunication company, the method is found to improve prediction accuracy significantly compared to other algorithms, such as artificial neural networks, decision trees, and logistic regression. The results also indicate that LOLIMOT can have accurate outcome in extremely unbalanced datasets.

An analysis and design framework for agent-based social simulation

Agent-based modeling is one of the popular tools for analyzing complex social systems. To model such systems, social attributes such as culture, law and institutions need to implemented as part of the context of a MAS, independently of individual agents. In this paper, we present MAIA; a framework for modeling agent-based systems based on the Institutional Analysis and Development Framework (IAD). The IAD is a well established comprehensive framework which addresses many social attributes. To make this framework applicable to agent-based software implementation, we inspire from some of the detailed definitions in the OperA methodology. The framework covers the different types of structures affecting agents at the operational level; physical, collective and constitutional. Moreover, this framework includes the conceptualization and design of evaluation. An agent-based methodology has also been developed from the MAIA framework which consists of two layers. A conceptualization layer for analyzing and decomposing the system and a detailed design layer which leads to the implementation of social models. MAIA allows the balance of global institutional requirements with the autonomy of individual agents thus enabling system evolution and reflecting more of reality in artificial societies.

Structuring Qualitative Data for Agent-Based Modelling

Using ethnography to build agent-based models may result in more empirically grounded simulations. Our study on innovation practice and culture in the Westland horticulture sector served to explore what information and data from ethnographic analysis could be used in models and how. MAIA, a framework for agent-based model development of social systems, is our starting point for structuring and translating said knowledge into a model. The data that was collected through an ethnographic process served as input to the agent-based model. We also used the theoretical analysis performed on the data to define outcome variables for the simulation. We conclude by proposing an initial methodology that describes the use of ethnography in modelling.

Shared strategies in artificial agent societies

A shared strategy is a social concept that refers to a type of behavioural pattern that is followed by a significant number of individuals although it is, prima facie, not associated with an obligation or a prohibition. E. Ostrom has argued in favour of the pertinence of social strategies for institutional design and evolution and proposed a characterization suggestive of formal treatment. However, shared strategies as such have not been explicitly used in the context of regulated MAS in spite of their relevance and their affinity to more standard normative notions, of which a rich tradition exists in MAS research. In this paper, we discuss the notion of shared strategy, characterize its distinguishing features, propose its formalization using a temporal epistemic logic, and explore its potential use in regulated multi-agent systems.

CMF: A framework to improve the management of customer churn

Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Many churn management models have been developed over the years, mostly focusing on prediction accuracy and not considering the range of parameters and processes essential to manage the system as a whole. This study presents CMF (Churn Management Framework), a framework which tries to covers most of the requirement of a churn management system. Some of the results indicate that the analysis of churn reasons can have a positive impact on system efficiency. Also the preprocessing requirements of such model differ from other prediction systems thus requiring close attention. In conclusion, the profit gain of a company applying CMF is proved to be much higher than the profit gain from an ordinary churn prediction program.

Collaborative team formation using brain drain optimization: a practical and effective solution

In the area of professional social networks, collaborative team formation with its NP-hard nature, has attracted the attention of many researchers. The purpose of this study is to find a collaborative team which covers required skills and minimizes the communication cost among team members. To solve this problem, BRADO (BRAin Drain Optimization), a recently-proposed meta-heuristic swarm-based algorithm which simulates the brain drain phenomenon, has been utilized. In order to evaluate BRADO, it has been applied in extensive experiments to the DBLP and IMDb datasets. Results demonstrate the effectiveness and superiority of the BRADO algorithm in comparison with PSO, GA, ICA, RarestFirst and EnhancedSteiner algorithms. Our findings lead us to believe that the BRADO algorithm can be a promising method in the context of team formation problem.

Self-organization in the commons: An empirically-tested model

A appropriate bottom-up rule system can support the sustainability of common-pool resources such as forests and fisheries. The process that leads to the developments of such institutional settings requires the considerations of multiple social, physical, and institutional factors over long time horizons. In this paper, we present the SONICOM model as a general exploratory model of CPR systems. The model can be configured to represent different CPR systems in order to explore what kind of institutional settings result in stable systems, i.e. situations where the resource and the appropriators are in a state of well-being. We use a large-N-dataset of CPR management institutions to validate the model. The results show numerous correlations between various parameters of the system such as rule compliance, social influence and resource growth rate which help explaining the process of institutional emergence as well as unveiling the conditions under which systems are stable.

Modelling socio-ecological systems with MAIA

Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs. Regional manure-based biogas production can be feasible in the Netherlands.We used the MAIA modelling framework to build an ABM of a biogas infrastructure.MAIA enables the incorporation of institutional aspects in ABM.MAIA is an integrated solution for addressing modelling challenges in SES.Future research: Automatic code generation; Dynamic feedback of simulation results.