Exploring Narrative Economics:A Novel Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel Laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narratives (i.e., the stories) heard, told, and believed by participants in those markets. Shiller argues that otherwise irrational and difficult-to-explain behaviors, such as investors participating in highly volatile cryptocurrency markets, are best explained and understood in narrative terms: people invest because they believe, because they have a heartfelt opinions, about the future prospects of the asset, and they tell to themselves and others stories (narratives) about those beliefs and opinions. In this paper we describe what is, to the best of our knowledge, the first ever agent-based modelling platform that allows for the study of issues in narrative economics. We have created this by integrating and synthesizing research in two previously separate fields: opinion dynamics (OD), and agent-based computational economics (ACE) in the form of minimally-intelligent trader-agents operating in accurately modelled financial markets. We show here for the first time how long-established models in OD and in ACE can be brought together to enable the experimental study of issues in narrative economics, and we present initial results from our system. The program-code for our simulation platform has been released as freely-available open-source software on GitHub, to enable other researchers to replicate and extend our workComment: To be presented at the 13th International Conference on Agents and Artificial Intelligence (ICAART2021), Vienna, 4th--6th February 2021. 18 pages; 14 figure

Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model

Renewable energy resources and energy-efficient technologies, as well as building retrofitting, are only some of the possible strategies that can achieve more sustainable cities and reduce greenhouse gas emissions. Subsidies and incentives are often provided by governments to increase the number of people adopting these sustainable energy efficiency actions. However, actual sales of green products are currently not as high as would be desired. The present paper applies a hybrid agent-based model (ABM) integrated with a Geographic Information System (GIS) to simulate a complex socio-economic-architectural adaptive system to study the temporal diffusion and the willingness of inhabitants to adopt photovoltaic (PV) systems. The San Salvario neighborhood in Turin (Italy) is used as an exemplary case study for testing consumer behavior associated with this technology, integrating social network theories, opinion formation dynamics and an adaptation of the theory of planned behavior (TPB). Data/characteristics for both buildings and people are explicitly spatialized with the level of detail at the block scale. Particular attention is given to the comparison of the policy mix for supporting decision-makers and policymakers in the definition of the most efficient strategies for achieving a long-term vision of sustainable development. Both variables and outcomes accuracy of the model are validated with historical real-world data

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided

D4.4 - Report on developed methodologies and models for techno-economic modelling of PEDs and the transition towards their realisation

Executive Summary: In the light of urgent need for decarbonisation of the building sector, techno-economic modelling of energy systems is an essential part of the planning process of urban development. Positive Energy Districts (PEDs) should contribute to this transformation towards less carbon-intensive and more energy independent urban areas. Therefore, this report presents the techno-economic models that have been developed throughout the Smart-BEEjS project to determine the energy infrastructure required to transform current districts into PEDs. This report reviews the existing models that focus on the district and neighborhood energy modelling (Section 2). The literature emphasises that existing models are not sufficient for PED planning, as PED analysis requires a large diversity of data inputs and have very specific modelling requirements. Moreover, it is important to further advance an integrated systems approach that brings together technoeconomic and social aspects with sufficient detail. Four modelling approaches address four different sectors of the PED's energy infrastructure that needsto be included in the planning of the transition from exiting districts to PEDs. The electricity based system, including local renewable energy generation and electricity-based heating and cooling is covered by a mixed integer linear programming approach to guarantee an optimal technology portfolio while ensuring a positive energy balance (Section 3.1). The heating and cooling system is focusing on district solutions such as the 4th generation district heating/cooling with a mathematical approach (Section 3.2). The energy efficiency uptake of the building stock is addressed by agent based modeling (Section 3.3). Finally, the electric vehicle related charging infrastructure is modelled using statistics on real data (Section 3.4). Furthermore, as sector coupling is highly important these days, the interconnections of the presented models are drawn (Section 3.5). The models include important social factors such as affordability, inclusiveness and energy justice that is often not the focus of mainstream techno-economic models. As affordability, inclusiveness and energy justice are cornerstones of the PED concept, the models aim to address those values. The combined model can holistically evaluate what energy infrastructure is needed to transition from current districts to high-performance PEDs

Data and Design: Advancing Theory for Complex Adaptive Systems

Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour. Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights. In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects. The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed. The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population. The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour

Optimizing the Implementation of Green Technologies Under Climate Change Uncertainty

In this study, we aim to investigate the application of the green technologies (i.e., green roofs (GRs), Photovoltaic (PV) panels, and battery integrated PV systems) under climate change-related uncertainty through three separate, but inherently related studies, and utilize optimization methods to provide new solutions or improve the currently available methodsFirst, we develop a model to evaluate and optimize the joint placement of PV panels and GRs under climate change uncertainty. We consider the efficiency drop of PV panels due to heat, savings from GRs, and the interaction between them. We develop a two-stage stochastic programming model to optimally place PV panels and GRs under climate change uncertainty to maximize the overall profit. We calibrate the model and then conduct a case study on the City of Knoxville, TN.Second, we study the diffusion rate of the green technologies under different climate projections for the City of Knoxville through the integration of simulation and dynamic programming. We aim to investigate the diffusion rates for PV panels and/or GRs under climate change uncertainty in the City of Knoxville, TN. We further investigate the effect of different and evaluate their effects on the diffusion rate. We first present the agent based framework and the mathematical model behind it. Then, we study the effects of different policies on the results and rate of diffusion.Lastly, We aim to study a Lithium-ion battery load connected to a PV system to store the excess generated electricity throughout the day. The stored energy is then used when the PV system is not able to generate electricity due to a lack of direct solar radiation. This study is an attempt to minimize the cost of electricity bill for a medium sized household by maximizing the battery package utilization. We develop a Markov decision processes (MDP) model to capture the stochastic nature of the panels\u27 output due to weather. Due to the minute reduction in the Li-ion battery capacity per day, we have to deal with an excessively large state space. Hence, we utilize reinforcement learning methods (i.e., Q-Learning) to find the optimal policy

Understanding Radicalisation

Given the considerable amount of effort and public resources invested in countering radicalisation, achieving a clearer understanding of what radicalisation is and of its causes is arguably a worthwhile and necessary endeavour. This thesis argues that such an understanding is lacking at present. Up until recently, researchers have relied upon interviews with current or former radicals in order to try and tease out those factors which might have contributed to radicalisation. As a result of the methodological approach, the focus has been upon individuals who are radicalised and their personal backgrounds, rather than on causal factors and mechanisms which might have been at work at other levels of analysis. Utilising and developing a tripartite theory of radicalisation by Bouhana and Wikström 2011, this thesis focuses on the emergence of radicalising settings. The role of so-called macro-level, or systemic, factors, which would affect the broader ecology and explain why settings propitious to radicalisation do or do not emerge in particular environments (e.g. communities) at particular times has been largely overlooked. One explanation is that such factors are rarely accessible through interviews conducted with those who commit terrorist acts. By using a relatively new methodology in the field, agent-based modelling, simulation experiments were conducted to examine the impact of collective efficacy and social disorganisation upon the emergence and maintenance of radicalisation within a setting. Systematic reviews were conducted in order to elucidate existing data for modelling parameters, while interviews with former radicals and current deradicalisation experts were carried out in order to provide new data for the model and add to a field in which this primary data is still limited. Agent-based modelling is shown to provide the field of radicalisation studies with a methodology in which to test and refine theory, scientifically examine current hypothesis and generate more by investigating potentially unexpected results from simulation experiments. This could be of great help to practitioners who seek to understand the impact of their interventions when conducting counter-radicalisation or de-radicalisation work in the future