The social life of the novel idea: What did social psychologists ever do for us?

Purpose - The paper presents the extant literature relating to the social processes of innovation in built environment design teams. The paper connects the relevant and significant work in the field of Social Psychology and Architecture, Engineering and Construction (AEC) to derive a theoretical framework which can be used to direct further research, towards development of the behavioural facet of design management. Design/methodology/approach - First, we establish which aspects of social processes of innovation are already present within the AEC field and examine concepts/ideas in Social Psychology that are likely to be important in understanding group processes within AEC, applying three emergent themes of 1) social climate; 2) risk attitudes and 3) motivation and reward. Second, we identify which elements of Social Psychology may be used to expand, consolidate and develop our understanding and identify gaps in AEC specific knowledge. Findings - The paper suggests that whilst the AEC literature has supplanted some key elements of Social Psychology, this discipline offers a further and significant theoretical resource. However, whilst some aspects of social climate and motivation/reward are well-represented in the AEC field, these have not yet been fully explored. Furthermore, how collective attitudes to risk can influence design decision-making is identified as having a limited presence. Originality/value - This paper is the first to bring together the two disciplines of AEC and Social Psychology to examine the social aspects of innovative design performance in built environment teams. The paper fulfils an identified need to examine the social processes that influence innovative design performance in constructio

A Multidimensional Framework for Financial-Economic Decisions

Most financial-economic decisions are made consciously, with a clear and constant drive to ???good???, ???better??? or even ???optimal??? decisions. Nevertheless, many decisions in practice do not earn these qualifications, despite the availability of financial economic theory, decision sciences and ample resources. We plea for the development of a multidimensional framework to support financial economic decision processes. Our aim is to achieve a better integration of available theory and decision technologies. We sketch (a) what the framework should look like, (b) what elements of the framework already exist and which not, and (c) how the MCDA community can co-operate in its development.decision making;finance;decision analysis;financial decisions;multiple criteria

Vaccine Risk Communication: Lessons from Risk Perception, Decision Making and Environmental Risk Communication Research

Dr. Bostrom reviews the rich variety of empirical findings available to guide risk communication and demonstrates how it can contribute to vaccine risk and safety communication

A review of Multi-Agent Simulation Models in Agriculture

Multi-Agent Simulation (MAS) models are intended to capture emergent properties of complex systems that are not amenable to equilibrium analysis. They are beginning to see some use for analysing agricultural systems. The paper reports on work in progress to create a MAS for specific sectors in New Zealand agriculture. One part of the paper focuses on options for modelling land and other resources such as water, labour and capital in this model, as well as markets for exchanging resources and commodities. A second part considers options for modelling agent heterogeneity, especially risk preferences of farmers, and the impacts on decision-making. The final section outlines the MAS that the authors will be constructing over the next few years and the types of research questions that the model will help investigate.multi-agent simulation models, modelling, agent-based model, cellular automata, decision-making, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Budgeted Reinforcement Learning in Continuous State Space

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an - adjustable - threshold. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving.Comment: N. Carrara and E. Leurent have equally contribute