Contested modelling

We suggest that the role and function of expert computational modelling in real-world decision-making needs scrutiny and practices need to change. We discuss some empirical and theory-based improvements to the coupling of the modelling process and the real world, including social and behavioural processes, which we have expressed as a set of questions that we believe need to be answered by all projects engaged in such modelling.  These are based on a systems analysis of four research initiatives, covering different scales and timeframes, and addressing the complexity of intervention in a sustainability context. Our proposed improvements require new approaches for analysing the relationship between a project’s models and its publics.  They reflect what we believe is a necessary and beneficial dialogue between the realms of expert scientific modelling and systems thinking.  This paper is an attempt to start that process, itself reflecting a robust dialogue between two practitioners sat within differing traditions, puzzling how to integrate perspectives and achieve wider participation in researching this problem space.&nbsp

Systems practice in engineering: reflections on doctoral level systems supervision

The Industrial Doctorate Centre (IDC) in Systems, a collaboration between the University of Bristol and the University of Bath, offers an Engineering Doctorate (EngD) in Systems Programme which is aimed at high-calibre engineers from graduate level to early/mid-career stage with the purpose of developing the systems-thinking capabilities of future leaders in industry. Research Engineers on this programme are based ~75% of their time in industry and focussed on a research project defined by their sponsoring company. This paper presents a personal reflection on the role of the systems supervisor on this programme with a focus on four areas of particular interest to the author i) alignment of industry needs and academic research, ii) developing an appreciation for the need for systems thinking, iii) navigating the systems literature, and iv) teaching research methods for doctoral research in systems. The purpose is to encourage and engage in debate on the development of systems practice in engineering

Epistemological Perspectives on Simulation. An Introduction

LNAI Book Serie

Ten challenges for computer models in transitions research: Commentary on Holtz et al

The emergence of a dedicated modelling community within the transitions field is to be welcomed, and the authors of a recent paper in EIST (Holtz et al., 2015) make many valuable points. We build on their position paper in two ways. First, we reflect on some of the ways in which modelling in other areas of 'sustainability science' has sometimes fallen short of the strengths articulated. Second, we extend some of Holtz et al.'s discussion of the epistemological and ontological challenges for modelling transitions. We suggest ten challenges in response to the more optimistic claims made by Holtz et al., and we provide some additional suggestions for ways forward. In particular, we suggest that seeking closer integration of qualitative, socio-technical analysis with models may not always be the best strategy. Rather, pluralist 'bridging strategies' and dialogue between analytic approaches may be more productive

A quantum theoretical explanation for probability judgment errors

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. Quantum probability theory is a general and coherent theory based on a set of (von Neumann) axioms which relax some of the constraints underlying classic (Kolmogorov) probability theory. The quantum model is compared and contrasted with other competing explanations for these judgment errors including the representativeness heuristic, the averaging model, and a memory retrieval model for probability judgments. The quantum model also provides ways to extend Bayesian, fuzzy set, and fuzzy trace theories. We conclude that quantum information processing principles provide a viable and promising new way to understand human judgment and reasoning