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Glioblastoma multiforme is a highly aggressive form of brain cancer, with a median survival time for diagnosed patients of 15 months. Treatment of this cancer is typically a combination of radiation, chemotherapy and surgical removal of the tumour. However, the highly invasive and diffuse nature of glioblastoma makes surgical intrusions difficult, and the diffusive properties of glioblastoma are poorly understood. In this paper, we introduce a stochastic interacting particle system as a model of in vitro glioblastoma migration, along with a maximum likelihood-algorithm designed for inference using microscopy imaging data. The inference method is evaluated on in silico simulation of cancer cell migration, and then applied to a real data set. We find that the inference method performs with a high degree of accuracy on the in silico data, and achieve promising results given the in vitro data set. [ABSTRACT FROM AUTHOR]

This paper studies the role of trade for the joint uptake of AI‐enabled automation in manufacturing and engineering. It develops an agent‐based model (ABM) where the agents are heterogeneous manufacturers and engineering firms. The ABM features two technology‐related business models: engineering as a face‐to‐face consultancy service and engineering as automated software. The software adoption rate follows an S‐shaped curve for manufacturers and a boom and bust cycle for engineers. In the early phase, shortage of engineers constrains AI uptake, while engineers become abundant when AI is fully adopted. Trade affects the cut‐off productivity level at which manufacturers switch technology, the shape of the adoption rate curve, and the incentives for engineers to develop software. Bulky transactions and different productivity distributions across countries are drivers of trade in their own right. [ABSTRACT FROM AUTHOR]

Growing cities need new residential areas, which are often either not connected to the existing transport infrastructure or are poorly connected to it. A fast way to connect these areas is the construction of roads. However, this generates a car-depending mobility among the inhabitants, which is in conflict with several sustainability goals. Moreover, the impact of the implementation of new public transport options is only partly known and this fact reduces the willingness to invest in expensive public transport measures. In this work we examine different mobility concepts, including shared mobility, bicycle highways, a high-frequency bus service, suburban trains and car limitations in a new residential area of 2000 households in Berlin, Germany, which is currently under construction. The households and inhabitants are created synthetically using statistical data derived from a survey among the first people moved in. The age and size structure of these households turn out to be different from the neighboring households. Then, we implement all measures in a microscopic travel demand simulation and quantify the potential modal shifts for four different mobility concepts. The results show that weak and short-term mobility concepts show no significant change in mobility behavior. Only highly integrated projects like bicycle highways into the inner city combined with suburban trains can reduce the need for car-dependent mobility. Shared mobility only fills in the gaps for special occasions but not for daily mobility due to the high costs. In a final step we examine the usage of the introduced public transport services and compare the change in the occupation of the buses and trains. Here our work shows that interchanging from bus to subways and suburban trains drastically reduces the attractiveness of public transport. Introducing a new suburban train changes this situation and the whole region shows a drop of 40% of car trips.

This study aims to integrate meta-analysis into agent-based models and provide foundational insights into biased agent interactions. It delves deeply into the effects of various behavioral biases such as anchoring, disposition effect, loss aversion, and others on market dynamics and investor decisions. Using agent-based models, it presents simulations of market scenarios and investor behaviors, emphasizing the impact of individual decisions on market dynamics. The innovative approach of this study lies in integrating behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen our understanding of market dynamics and investor behaviors, which can be helpful in financial market analysis and policy-making. This study aims to provide a foundational framework for those looking to integrate meta-analysis into agent-based models and explore biased agent behaviors. The findings demonstrate the ability to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias taking into account agents' socio-demographic and psychological factors, as close to the real world as possible. The results offer highly favorable forecasts for modeling human behaviors more accurately in portfolio optimizations and for expanding the applications of Generalized Artificial Intelligence in financial market implementations.

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly changing pandemic that requires different mitigation strategies to maintain clinical preventive services, including vaccination. Countries around the world have started administering the authorised COVID-19 vaccines. However, the number of positive cases remains at high levels. This paper simulates the transmission of COVID-19 over time using a hybrid simulation model, consisting of agent-based and discrete event simulation approaches. The model allows simulating the spread of the virus under various parameters, such as hospital capacities and vaccination policy, to observe the effects of each parameter on the virus transmission. The model and results can provide insights on how the daily vaccination rates and available medical capacity affect the number of deaths and infections. The model can be used to determine the required medical resources and vaccine supplies over time not only for COVID-19 but also for future pandemics with similar characteristics. [ABSTRACT FROM AUTHOR]

In the literature, human values are defined as desirable, trans-situational goals serving as guiding principles in people’s lives. Schwartz introduced the concept of ten different values that are grouped into four higher order values: openness to change, conservation, self-transcendence, and self-enhancement. Some of the Schwartz values will underlie acting for one’s own good, the others for the good of the community. The collective output of the community will depend on these two types of actions and the relations between them. Acting for the benefit of others may, yet does not have to, increase the total benefit for the community, even when it leads to less benefit to the self. In this paper we provide an analogy for the mechanisms underlying the relation between Schwartz values and economic output. The observed economic output is a result of the behaviour of many heterogeneous agents interacting with each other. The main problem is to verify whether and how the differences in the distributions of Schwartz values in a given community may influence its collective economic output. We classify Schwartz values into two different groups, based on the different effects these values may have on the observed collective output. A higher importance of self-enhancement (power, achievement, and hedonism) and conservation (security, tradition, and conformity) increases working time in the public sector and the public goods return, while simultaneously lowering working time in the private sector and the private goods return. The values of openness to change (stimulation and self-direction) and self-transcendence (universalism and benevolence) have the opposite effect. [ABSTRACT FROM AUTHOR]

This paper introduces a new multi-agent-based tumour-site compartment model for human cancer. We analyze the qualitative behaviour of the model, ensuring positivity and boundedness, and discuss equilibria and their local stability using the Jacobian matrix. Moreover, we address a cancer containment procedure through optimal drug administration, aiming to eradicate tumour cells within an optimal timeframe by employing optimal control theory and Pontryagin's maximum principle. The paper concludes with a simulation validating our theoretical claims and providing valuable insights for future research in cancer therapeutics. [ABSTRACT FROM AUTHOR]

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The utilization of simulation modelling in supply chain management has proven to be a valuable tool for logistics service providers, enabling them to optimize their operations and meet customer demands efficiently. AnyLogic, with its diverse capabilities in discrete event simulation, system dynamics, and agent-based simulation, stands out as a powerful software solution for tackling complex supply chain challenges. This paper significantly contributes by demonstrating the practical implementation of AnyLogic in the modelling and simulation of agro-food product distribution network, particularly vegetable oils from the Cevital group. Additionally, the study integrates an optimization experiment to ascertain the optimal number of vehicles required to meet a specific customer demand while ensuring a high level of fleet utilization. The obtained results are very encouraging and demonstrate the feasibility and effectiveness of our approach.

Agent based modelling (ABM) offers a powerful abstraction for scientific study in a broad range of domains. The use of agent based simulators encourages good software engineering design such as separation of concerns, that is, the uncoupling of the model description from its implementation detail. A major limitation in current approaches to ABM simulation is that of the trade off between simulator flexibility and performance. It is common that highly optimised simulations, such as those which target graphics processing units (GPU) hardware, are implemented as standalone software. This work presents a software framework (FLAME GPU 2) which balances flexibility with performance for general purpose ABM. Methods for ensuring high computational efficacy are demonstrated by, minimising data movement, and ensuring high device utilisation by exploiting opportunities for concurrent code execution within a model and through the use of ensembles of simulations. A novel hierarchical sub‐modelling approach is also presented which can be used to model certain types of recursive behaviours. This feature is shown to be essential in providing a mechanism to resolve competition for resources between agents within a parallel environment which would otherwise introduce race conditions. To understand the performance characteristics of the software, a benchmark model with millions of agents is used to explore the use of simulation ensembles and to parametrically investigate concurrent code execution within a model. Performance speedups are demonstrated of 3.5×$$ \times $$ and 10×$$ \times $$ respectively over a baseline GPU implementation. Our hierarchical sub‐modelling approach is used to demonstrate the implementation of a recursive algorithm to resolve competition of agent movement which occurs as a result of agent desire to simultaneously occupy discrete areas high in a 'resource'. The algorithm is used to implement a classical socio‐economics model, Sugarscape, with populations of up to 16M agents. [ABSTRACT FROM AUTHOR]

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents' profiles and incorporates the parameterisation of agents' behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles. [ABSTRACT FROM AUTHOR]

Os caóticos processos de urbanização no Brasil acarretaram em inúmeros problemas no espaço urbano atual, como o espraiamento das cidades. A partir desta problemática, emerge a necessidade de se aperfeiçoar métodos de planejamento urbano capazes de garantir o desenvolvimento sustentável. Nesse sentido, entendendo que uma possível solução seja a densificação urbana, o presente trabalho com recorte no bairro Santa Maria em Uberlândia-MG, Brasil, simula com base em agentes a repercussão desta densificação, mais especificamente seus impactos diretos no trânsito local. Os resultados obtidos expressam que a infraestrutura urbana atual não seria capaz de comportar tal crescimento populacional, ocasionando novos problemas como conurbação e sobrecarregamento dos serviços. Portanto, apesar de assertivo, o adensamento deverá ocorrer em etapas e com distribuição uniforme pela cidade, garantindo que nenhuma região sobrecarregue outra para compensar.

Particle systems made up of interacting agents is a popular model used in a vast array of applications, not the least in biology where the agents can represent everything from single cells to animals in a herd. Usually, the particles are assumed to undergo some type of random movements, and a popular way to model this is by using Brownian motion. The magnitude of random motion is often quantified using mean squared displacement, which provides a simple estimate of the diffusion coefficient. However, this method often fails when data is sparse or interactions between agents frequent. In order to address this, we derive a conjugate relationship in the diffusion term for large interacting particle systems undergoing isotropic diffusion, giving us an efficient inference method. The method accurately accounts for emerging effects such as anomalous diffusion stemming from mechanical interactions. We apply our method to an agent-based model with a large number of interacting particles, and the results are contrasted with a naive mean square displacement-based approach. We find a significant improvement in performance when using the higher-order method over the naive approach. This method can be applied to any system where agents undergo Brownian motion and will lead to improved estimates of diffusion coefficients compared to existing methods. [ABSTRACT FROM AUTHOR]

We introduce an agent based model framework to investigate how an alternative to classic image score and gossip can support the emergence of cooperation in a repeated prisoner dilemma game with agents employing mixed strategies. We debate the universality of image scores, arguing that they cannot be considered an objective property of the agents observed but rather a subjective property of each observer. From this assumption, we develop a private list mechanism for opponent selection and gossip sharing among the population of the simulation. The results show that the private list mechanism is able to foster the emergence of cooperation, and that for various levels of list usage different levels of cooperation correspond in the system. Finally, we observe interesting topological properties emerging, with networks characterised by one ‘super-hub’ connected to every other node, suggesting the emergence of centralized entities to support cooperation.

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures.

Activity-based models predict locations at TAZ (travel analysis zone) level. Recent research questions about carpooling, ride sharing, vehicle sharing, multi-modality, MaaS,... require space resolution at street address level. Pitfalls related to spatial resolution adaptation are identified and a disaggregation method beyond sampling from uniform distributions is presented. Daily travel plans generated by the FEATHERS activity based model for The Netherlands are presented and compared to observations. [ABSTRACT FROM AUTHOR]

Smart farming is essential to increasing crop production, and there is a need to consider the technological advancements of this era; modern technology has helped us to gain more accuracy in fertilizing, watering, and adding pesticides to the crops, as well as monitoring the conditions of the environment. Nowadays, more and more sophisticated sensors are being developed, but on a larger scale, agricultural networks and the efficient management of them is very crucial in order to obtain proper benefits from technology. Our idea is to achieve sustainability in large-scale farms by improving communication between wireless sensor nodes and base stations. We want to increase communication efficiency by introducing machine learning algorithms. Reinforcement learning is the area of machine learning which is concerned with how involved agents are supposed to take action in specified environments to maximize reward and achieve a common goal. In our network, a large number of sensors are being deployed on large-scale fields; reinforcement learning is used to find the optimal set of paths towards the base station. After a number of successful paths have been developed, they are then used to transmit the sensed data from the fields. The simulation results have shown that in larger scales, our proposed model had less transmission delay than the shortest path transmission model and broadcasting techniques that were tested against the data transmission paths developed by reinforcement learning. [ABSTRACT FROM AUTHOR]

The complicated urban settlement pattern is the cause of many problems in the urban area. These settlement patterns can be understood by studying human decisions made while developing the land parcel. In this study, we have proposed an Agent Based Model (ABM) of residential location choice. The model takes social-economic information of households and the development suitability as input data. The household-level information is interpolated with census and annual health survey data using Iterative proportion fitting. The development suitability is generated by regression of development affecting parameters such as Road network, Water supply, Amenities, and Land Rates. ABM for residential location choices is simulated for 2003–2019 time period. For accuracy assessment, the predicted development is compared with actual development. The random forest regression-based model resulted with 60% accuracy and logistic regression model resulted with 53% accuracy. After comparing location suitability and residential choices of different social groups, a divide based on socio-economic status is seen in the city. The location of different amenities and services shapes the density and neighbourhood character of that area. [ABSTRACT FROM AUTHOR]

Driven by rapid industrialization and urbanization, increased carbon emissions in urban transportation pose a significant environmental challenge. Promoting public transportation and managing travel structures are essential for reducing emissions. Despite being proposed as an innovative solution, the urban implementation of carbon charge policies remains limited. This study explores carbon charge policies and analyzes pathways to shift car travel to low-carbon public transportation modes. Dynamic changes in emission hot spots are captured to define the carbon charge zones utilizing the emerging hot spot analysis method, which considers spatiotemporal autocorrelation and statistical validation. By integrating carbon charge into the Multi-Agent Transport Simulation (MATSim) platform, this study overcomes traditional policy tools' limitations in capturing traffic dynamics and mode substitution effects. Experiments conducted in Shenzhen's Futian District demonstrate the effectiveness of carbon charge policies in reducing car emissions, particularly during evening peak hours. The findings provide insights for sustainable urban transportation planning and management. [ABSTRACT FROM AUTHOR]

Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions. [ABSTRACT FROM AUTHOR]

The re-emerging outbreak of COVID-19 in Beijing, China, in the summer of 2020 originated from a SARS-CoV-2-infested wholesale food supermarket. We postulated that the Xinfadi market outbreak has links with food-trade activities. Our Susceptible to the disease, Infectious, and Recovered coupled Agent Based Modelling (SIR-ABM) analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number (R0). The retail shop closure strategy reduced the number of visitors to the market by nearly half. In addition, the buy-local policy option reduced the infection by more than 70% in total. Therefore, retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents’ profiles and incorporates the parameterisation of agents’ behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.

From a climate change perspective, the governance of natural common-pool resources—the commons—is a key point in the challenge of transitioning to sustainability. This paper presents the main strategic advances of the São Paulo Urban Living Laboratory (ULL) regarding Food, Energy and Water (FEW Nexus) analysis and modelling at the border of a high biodiverse forest in a peri-urban region in southeast Brazil. It is a replicable and scalable method concerning FEW governance. The FEW Nexus is an analytical guide to actions that will enable a colossal set of innovative processes that the transition to sustainability presupposes. Sustainable governance of the FEW dimensions, seen as an innovation-based process, is approached by a decision making tool to understand the past and future dynamics of the system. The governance framework is based on a multi-criteria and multi-attribute set of sustainability-relevant factors used as indicators to model complex system dynamics (SD) and the stakeholders' future expectations through a Delphi approach. Based on the three main dimensions of the Ecosystem Services Approach—Physical and Material Conditions, Attributes of Communities, and Rules-in-Use—the tool comprises thirteen specific sustainability indicators such as water and carbon footprints, land use social development, payment for ecosystem services, and land use gain indices. Its development was designed to generate a long-term network of socioenvironmental stakeholders' decision making processes and collective learning about a higher level of sustainable systems. System Dynamics modelling demonstrates the associations between sustainability indicators and the impacts of payment for ecosystem services on the land use social development index, or on the trophic state index. The Delphi foresight approach, using the Promethee-Gaia method, allows us to understand the positions of multiple agents regarding the transition process. In this context, decision making tools can be very useful and effective in answering the "how to" questions of ULLs and paving the way for transition, providing collective planning and decision support frameworks for sustainability transition management. [ABSTRACT FROM AUTHOR]

Since the rapid growth of urban populations, the study of urban systems has gained considerable attention from researchers, decision makers, governments, and organizations. Urban systems are complex and dynamic such that they produce emergent patterns such as self-organization and nonlinearity. Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities. However, agent-based models are difficult to effectively optimize and validate without high quality real-world data. Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies. Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems. This paper provides a general overview of concepts, review of recent applications, and discussion of challenges and opportunities in the context of using geosocial media data in agent-based models for urban systems. We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data, given that research moves towards standard guidelines that enable the comparison and effective use of ABMs, and geosocial media data under appropriate circumstances and applications.

Evacuation is an important issue during the occurrence of an earthquake, due to the influence of people’s responsive behaviours to the disaster. This indicates the occurrence of overcrowded conditions, which causes the conflict of people’s movement. It is also one of the main reasons the process of evacuation is difficult, leading to the necessity to specifically model the movement within each building. Therefore, this study aims to analyze the earthquake evacuation modelling in Faculty of Engineering building, University of Bengkulu, Indonesia. This used a multi-agent programmable modelling environment known as NetLogo, which adopted an ABM model that is often utilized to observe elemental movement response. The agents occupying the building also moved to the stairs and evacuation doors, for exit towards the assembly point, which was located in front and behind Faculty of Engineering. The agent-based modelling is then conducted by inputting the layout of the building and the number of occupants in each room. Furthermore, the simulations were performed by considering various agents’ capacity in the building, during the evacuation. The results showed the time taken for the agents to exit the building during the disaster. This confirmed that the required exit time generally decreased with the increasing population percentage in the building. During this process, the conflict point also occurred around the corridor of Floors 1 and 2 (T1, T2 and T3), as well as the exits of the building (P1, P2, P3, and P4). This was due to the occurrence of a high density when agents carry out evacuation movements. Based on these results, the placement of signs was recommended, as guidelines during the evacuation process.

This paper considers whether existing approaches for quantifying variables in cellular automata (CA) modelling adequately incorporate all the relevant factors in typical actor decisions underpinning urban development. A survey of developers and planners is used to identify factors they incorporate to allow for or proceed with development, using South East Queensland as a reference region. Three types of decision factors are identified and ranked in order of importance: those that are already modelled in CA applications; those that are not modelled but are quantifiable; and those that are not (easily) quantifiable because they are subjective in nature. Factors identified in the second category include development height/scale, open space supply, and existing infrastructure capacity. Factors identified in the third category include political intent, community opposition, and lifestyle quality. Drawing on our analysis of these factors we suggest how and to what extent survey data might be used to address the challenges of incorporating actor variables into the CA modelling of urban change. The paper represents the first attempt to review what decision factors should be included in CA modelling, and how this might be enabled. [ABSTRACT FROM AUTHOR]

The goal of this paper is to verify how behavioural and social features influence the decisions of market makers and to identify the impact that such decisions have on the trade volume, price levels and bid-ask spreads observed in the market. Many classical microstructural models explain the rational behaviour of market makers. We can distinguish two main groups of such models: information-based models and inventory-based ones. On the one hand, information-based models emphasise the role of the informational asymmetry between market makers and informed traders. On the other hand, inventory-based models relate the behaviour of market makers to the current status of inventories. In particular, the objective of the market maker is to either avoid bankruptcy or merely maintain an inventory balance. It is known, however, that it is not just rational factors which may influence economic behaviour. In particular, based on the literature, we stipulate that such personal traits as risk attitude, impulsivity, social behaviour and sense of fairness may influence market makers' behaviour. To verify this, we conducted a series of experiments with the goal of analysing the relation between these factors and market maker behaviour. Participants in these experiments played the role of a market maker in the simulated options market. Experimental data enabled us to analyse how the behavioural traits influence individual decisions. However, due to missing interactions among market makers during the experiment, the data did not enable us to analyse how these traits influence the aggregated observed markets parameters, such as trading volume and mean spread in a competitive market. Therefore, we propose a formal agent-based model that makes it possible to investigate how behavioural factors influence market variables such as price and volume by means of the simulation. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a simple interactive agent mechanism, where the distribution of returns generated from the mechanism match stylized facts in financial markets. We introduce one more key factor, the length of time horizon on performance evaluations between strategies, which also has a significant influence on price fluctuations. To investigate the transitions among states, we introduce a Markov transition matrix, Perron‐Frobenius transition matrix, and Inertia. Our simulation results show the stickiness of states switching from one to another, and the longer length of time horizon on performance evaluations would generate more complex dynamic price fluctuations. We link our simple heterogeneous agent mechanism with Markov trajectory entropy and provide a total score and probability density functions of representations under two states as applications for the mechanism. [ABSTRACT FROM AUTHOR]

Understanding the evolution and spread of opinions within social groups gives important insight into areas such as public elections and marketing. We are specifically interested in how psychological theories of interpersonal influence may affect how individuals change their opinion through interactions with their peers, and apply Agent-Based Modelling to explore the factors that may affect the emergence of consensus. We investigate the co-evolution of opinion and location by extending the Deffuant–Weisbuch bounded confidence opinion model to include mobility inspired by the psychological theories of homophily and dissonance, where agents are attracted or repelled by their neighbours based on the agreement of their opinions. Based on wide experimentation, we characterize the time it takes to converge to a steady state and the local diversity of opinions that results, finding that homophily leads to drastic differences in the nature of consensus. We further extend our mobility model and add noise in order to check the model's robustness, finding that a number of opinion clusters survive even with high levels of noise.