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In this work, a system of non‐linear difference equations is employed to model the opinion dynamics between a small group of agents (the target group) and a very persuasive agent (the indoctrinator). Two scenarios are investigated: the indoctrination of a homogeneous target group, in which each agent grants the same weight to his (or her) partner's opinion and the indoctrination of a heterogenous target group, in which each agent may grant a different weight to his or her partner's opinion. Simulations are performed to study the required times by the indoctrinator to convince a group. Initially, these groups are in a consensus about a doctrine different to that of the ideologist. The interactions between the agents are pairwise. [ABSTRACT FROM AUTHOR]

The paper reexamines an agent-based model of opinion formation under bounded confidence with heterogeneous agents. The paper is novel in that it extends the standard model of opinion dynamics with the assumption that interacting agents share the desire to exchange opinion. In particular, the interaction between agents in the paper is modeled via a dynamic preferential-matching process wherein agents reveal their preferences to meet according to three features: coherence, opinion difference, and agents' positive sentiments towards others. Only preferred matches meet and exchange opinion. Through an extensive series of simulation treatments, it follows that the presence of sentiments, on one hand, hardens the matching process between agents, which leads to less communication. But, on the other hand, it increases the diversity in preferred matches between agents and thereby leads to a better-integrated social network structure, which reflects in a reduction of the opinion variance between agents. Moreover, at combinations of (a) high tolerance, (b) low sensitivity of agents to opinion volatility, and (c) low levels of confidence, agents are occasionally drawn away from the consensus, forming small groups that hold extreme opinions. [ABSTRACT FROM AUTHOR]

Agent-Based Models (ABMs) have been adopted to simulate very different kinds of complex systems, from biological systems to complex coupled human-natural systems. In particular, when used to simulate man-managed systems, they have the advantage of allowing human behavioral aspects to be considered in the modelling framework. This paper provides a literature review of the application of ABMs for agricultural and land use modelling. One section is specifically devoted to the coupling of ABMs and Life Cycle Assessment (LCA) models. The aim of the paper is to give a perspective of the different “modelling blocks” one needs to take into account to build an ABM, dealing with general issues that must be considered regardless of the domain of application (such as validity, uncertainty, parameter sensitivity, agent definition, data provision), and providing concrete examples related specifically to ABMs applied to agricultural and land use modelling. The paper highlights the difficulties that the modelers can encounter in dealing with each of these modelling blocks, and presents the solutions that can be envisioned (mentioning those that have been applied in certain cases in the literature). As a general conclusion, we can observe that solutions based on complex systems simulations are starting, to some extent, to be influential in policymaking; however, practical user-friendly tools that allow scenario simulations also to non-expert users are clearly still lacking.

Editors play a critical role in the peer review system. How do editorial behaviors affect the performance of peer review? No quantitative model to date allows us to measure the influence of editorial behaviors on different peer review stages such as, manuscript distribution and final decision making. Here, we propose an agent-based model in which the process of peer review is guided mainly by the social interactions among three kinds of agents representing authors, editors and reviewers respectively. We apply this model to analyze a number of editorial behaviors such as decision strategy, number of reviewers and editorial bias on peer review. We find out that peer review outcomes are significantly sensitive to different editorial behaviors. With a small fraction (10 %) of biased editors, the quality of accepted papers declines 11 %, which indicates that effects of editorial biased behavior is worse than that of biased reviewers (7 %). While several peer review models exist, this is the first account for the study of editorial behaviors that is validated on the basis of simulation analysis. [ABSTRACT FROM AUTHOR]

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM. [ABSTRACT FROM AUTHOR]

Recent trends in the market for corporate bonds -such as its increase in size, the significant presence of low-quality debt instruments and the trading activity of non-financial corporations- call for the need of an investigation of the possible implications on the real economy. Although the phenomenon is still minor, the present paper aims at giving some early insights concerning the introduction of a corporate bond market, where also nonfinancial firms can invest. To do so, we build on an existing macroeconomic agent-based model of the CATS tradition and we introduce the possibility for firms to raise money through an alternative credit channel, i.e., the corporate bond market- and the opportunity for households and firms to invest part of their financial wealth into a portfolio of Government bonds and corporate bonds. Experiments based on single runs or on 100 Monte Carlo simulations suggest that the introduction of a bond market may exacerbate existing crisis and recessions and that when firms buy bonds, inequality may escalate. To the best of our knowledge, this paper constitutes one of the first attempts to incorporate a corporate bond market into a macro ABM. [ABSTRACT FROM AUTHOR]

Globally, high-speed rail systems serve nearly 2 billion passenger-km daily. By virtue, they are a critical infrastructure like telecommunication and power networks. Accordingly, they become a catalyst for societal and economic growth stemming from the mobility business. The highspeed rail operations are very complex and interdependent, owing to the escalated demands for longdistance interconnected transportation. In recent years, there have been unreasonable delays for passengers as a new norm due to unfortunate train cancellations and relaxation of mobility performance requirements. Therefore, accurate measurements, monitoring and prediction of disruptive impacts and service performance metrices are indispensable. Within the scope of high-speed rail services, this paper examines how agent-based and multi-agent-based models are utilized to address such the challenges. Our findings reveal that the current use of agents or multi-agent models has some limitations for practical applications. Previous studies showed that mathematical methods to assess the resilience of critical infrastructures, railway scheduling, and vehicle dispatching can yield more satisfactory outcomes, although the approaches can be relatively time-consuming. In contrast, agent-based and multi-agent-based models can shorten processing time and uncover disruptive events more promptly. The paper thus showcases several emerging concepts, including i) the utilization of big data for crisis management, ii) interconnectivity analysis of high-speed rail infrastructures, and iii) enhancement of transport resilience. In addition, our findings identify the most influential agents and their possible applications to enhance systems resilience of highspeed rail networks when dealing with unforeseen physical and cyber threats. [ABSTRACT FROM AUTHOR]

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. [ABSTRACT FROM AUTHOR]

The pricing behavior of agricultural processing firms in input markets has large impacts on farmers' and processors' prosperity as well as the overall market structure. Despite analytical approaches to food processors' pricing in agricultural input markets, the need for models to represent complex market features is urgent. Agent-based models (ABMs) serve as computational laboratories to understand complex markets emerging from autonomously interacting agents. Yet, individual agents within ABMs must be equipped with intelligent learning algorithms. In this paper, we propose supervised and unsupervised learning agents to simulate the pricing behavior of firms in agricultural markets' ABMs. Supervised learning firms are pre-trained to accurately best respond to their competitors and are deemed to result in the market Nash equilibria. Unsupervised learning firms play a course of pricing interaction with their competitors without any pre-knowledge but based on deep reinforcement learning. The simulation results show that unsupervised deep learning firms are capable of approximating the pricing equilibria obtained by the supervised firms in different spatial market settings. Optimal discriminatory and uniform delivery pricing emerges in agricultural input markets with the high and intermediary importance placed on space. Free on board pricing emerges in agricultural input markets with small importance placed on space. [ABSTRACT FROM AUTHOR]