The dynamics of consensus in group decision making: investigating the pairwise interactions between fuzzy preferences.

In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear nature of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations.

An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

Mean-Field Theory of Meta-Learning

We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.Comment: 23 page

Group facilitation skills for participatory decision-making: report of a follow-up outcome evaluation

This working paper reports on an evaluation designed to assess the usefulness of the skills gained during the training course, if the skills learned have been applied and, if so, how the new facilitation tools and techniques have changed meeting processes. The evaluation also identified obstacles to the use of new skills faced by participants, additional follow-on activities that have been conducted by participants, and other related skills that are needed to complement what was learned in the ILAC facilitation course

Theorizing EU trade politics

This special issue aims to take the first step towards an inter-paradigmatic debate in the study of European Union trade politics

Collective dynamics of belief evolution under cognitive coherence and social conformity

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework explains how social instabilities can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We therefore argue that the inclusion of cognitive factors into a social model is crucial in providing a more complete picture of collective human dynamics

Triple Task and the Philosophers Stone: discovering a methodology for systemic and reflective participation

The European Union Framework Package 7 project POINT (Policy Influence of Indicators) is exploring the use of indicators in several domains (most specifically sustainable development) in order to see how their value and ultimate usefulness can be maximised. One key aspect of POINT is to assess the ways in which groups and communities work to gain greatest use of information. Using an innovative methodology called 'Triple Task', the authors are applying a three cornered approach in order to gain an understanding as to how groups work, how they assesses themselves and how they appear to function from an external perspective. In this paper, the three stages of Triple Task are described and explored. Task One is effectively an adapted 'soft systems' approach, encouraging a group to work together on problem identification and action planning. Task 2 is a reflective, 'outside in', external review of group dynamics which makes use of the 'BECM' matrix for group systemic assessment first developed by the Systems Group at the UK Open University. Task 3 is an 'inside-out' self-reflective group analysis applying the well-known SYMLOG method. By use of a tri-analysis involving both qualitative and quantitative approaches, the authors show how during Triple Task managed events a 'story' emerges of group learning and development and, how a potential diagnostic tool for educing purposeful group behaviour has emerged. The research is in its early stages, but following the analysis of numerous groups from a range of sectors from across the European Union the authors are gaining clarity over what features are most consistent between purposeful group behaviour and group makeup. This is leading towards the development of a 'Triple Task' heuristic device for measuring and even predicting the systemic and reflective capacities of specific groups and communities and this could in turn result in means for improving participative effectiveness in a wide range of social engagements.