Assessing Simulations of Imperial Dynamics and Conflict in the Ancient World

The development of models to capture large-scale dynamics in human history is one of the core contributions of cliodynamics. Most often, these models are assessed by their predictive capability on some macro-scale and aggregated measure and compared to manually curated historical data. In this report, we consider the model from Turchin et al. (2013), where the evaluation is done on the prediction of "imperial density": the relative frequency with which a geographical area belonged to large-scale polities over a certain time window. We implement the model and release both code and data for reproducibility. We then assess its behaviour against three historical data sets: the relative size of simulated polities vs historical ones; the spatial correlation of simulated imperial density with historical population density; the spatial correlation of simulated conflict vs historical conflict. At the global level, we show good agreement with population density ($R^2 < 0.75$), and some agreement with historical conflict in Europe ($R^2 < 0.42$). The model instead fails to reproduce the historical shape of individual polities. Finally, we tweak the model to behave greedily by having polities preferentially attacking weaker neighbours. Results significantly degrade, suggesting that random attacks are a key trait of the original model. We conclude by proposing a way forward by matching the probabilistic imperial strength from simulations to inferred networked communities from real settlement data

An agent-based model of civil violence with imprisonment delay and legitimacy feedback

Epstein's Agent-Based model of civil violence has been very successful due to its simplicity and explanatory power, but does not represent important phenomena, such as processes operating at multiple scales and feedback mechanisms. In this work, we present an extension of Epstein's model that includes the effects of imprisonment delay, media coverage and feedback of rebellion bursts of the government's legitimacy. These innovations are relevant for a more realistic modeling of the complex and path-dependent effect of protests and violent confrontations on the evolution of the social context. The resulting simulations showed punctuated equilibrium as in Epstein's model, but the violence bursts lasted longer and displayed more complicated structure and interdependence on previous events. The rebellion peaks lead to drops and lowering of the time-averaged value of the government's legitimacy.info:eu-repo/semantics/acceptedVersio

Agent-based modeling of social conflict, civil violence and revolution: State-of-the-art-review and further prospects

In this paper, we present a state-of-the-art review of Agent-based models (ABM) for simulation of social conflict phenomena, such as peaceful or violent street protests, civil violence and revolution. First, a simplified characterization of social conflict phenomena as emergent properties of a complex system is presented, together with a description of their macro and micro levels and the scales of the emergent properties. Then, existing ABM for simulation of crowd dynamics, civil violence and revolution are analyzed and compared, using a framework that considers their purpose/scope, environment representation, agent types and their architecture, the scales of the emergent properties, the qualitative and quantitative understanding of the phenomena provided by the results obtained from the models. We discuss the strengths and limitations of the existing models, as well as the promising lines of research for filling the gaps between the state-of-the-art models and real phenomena. This review is part of a work in progress on the assembling and dynamics of protests and civil violence, involving both simulation of the assembling process and the protest dynamics, as well as data collection in real protest events, and provides hints and guidelines for future developments.info:eu-repo/semantics/publishedVersio

Simulation Modeling for Robust and Just Public Policy Decision-Making

Public policy decision-making is challenging for several reasons. First, the outcomes of pulling a public policy lever are often deeply uncertain because of the complexity of the social and physical systems involved. Second, even if outcomes can be predicted, there are multiple points of view to consider, and the same outcome can be viewed anywhere from very positively to very negatively by different stakeholders. Because of this, public policy decisions should be both robust and just. Robustness helps with the uncertainty in outcomes and justice helps with differences in worldview. In this dissertation, I employ system dynamics and agent-based simulation modeling techniques to assist decision-making in two public policy contexts: COVID-19 non-pharmaceutical interventions and police funding. I also develop a framework in which both robustness and justice can be handled simultaneously in complex public policy problems

Hybrid dragonfly algorithm with neighbourhood component analysis and gradient tree boosting for crime rates modelling

In crime studies, crime rates time series prediction helps in strategic crime prevention formulation and decision making. Statistical models are commonly applied in predicting time series crime rates. However, the time series crime rates data are limited and mostly nonlinear. One limitation in the statistical models is that they are mainly linear and are only able to model linear relationships. Thus, this study proposed a time series crime prediction model that can handle nonlinear components as well as limited historical crime rates data. Recently, Artificial Intelligence (AI) models have been favoured as they are able to handle nonlinear and robust to small sample data components in crime rates. Hence, the proposed crime model implemented an artificial intelligence model namely Gradient Tree Boosting (GTB) in modelling the crime rates. The crime rates are modelled using the United States (US) annual crime rates of eight crime types with nine factors that influence the crime rates. Since GTB has no feature selection, this study proposed hybridisation of Neighbourhood Component Analysis (NCA) and GTB (NCA-GTB) in identifying significant factors that influence the crime rates. Also, it was found that both NCA and GTB are sensitive to input parameter. Thus, DA2-NCA-eGTB model was proposed to improve the NCA-GTB model. The DA2-NCA-eGTB model hybridised a metaheuristic optimisation algorithm namely Dragonfly Algorithm (DA) with NCA-GTB model to optimise NCA and GTB parameters. In addition, DA2-NCA-eGTB model also improved the accuracy of the NCA-GTB model by using Least Absolute Deviation (LAD) as the GTB loss function. The experimental result showed that DA2-NCA-eGTB model outperformed existing AI models in all eight modelled crime types. This was proven by the smaller values of Mean Absolute Percentage Error (MAPE), which was between 2.9195 and 18.7471. As a conclusion, the study showed that DA2-NCA-eGTB model is statistically significant in representing all crime types and it is able to handle the nonlinear component in limited crime rate data well

State of the Art in Agent-Based Modeling of Urban Crime: An Overview

OBJECTIVES: Agent-based modeling (ABM) is a type of computer simulation that creates a virtual society and allows controlled experimentation. ABM has the potential to be a powerful tool for exploring criminological theory and testing the plausibility of crime prevention interventions when data are unavailable, when they would be unethical to collect, or when policy-makers need an answer quickly. This paper takes stock of the current literature to discuss the potential contributions of ABM, assess current practice, identify shortcomings that threaten the validity of findings using ABM, and to make suggestions regarding the construction and communication of future work using ABM. METHODS: We systematically searched major databases to find all publications using ABM to simulate urban crime patterns and coded publications to quantify the following information: (1) characteristics of the publication, the model and the agents, (2) model purpose, (3) crime type investigated, and (4) interrogation of the model via sensitivity testing and validation. RESULTS: After sifting papers according to our inclusion criteria, we identified and reviewed 45 publications. Models informed by the opportunity theory framework dominated. Most publications lacked detail sufficient to enable replication. Many did not include clear a rationale for modeling choices, parameter selection or calibration. Rarely were parameters calibrated using empirical data. Model validation was limited and inconsistent across papers. CONCLUSIONS: ABM offers significant potential for criminological enquiry. However, at present, the lack of model detail reported in publications makes it difficult to assess where sufficient evidence exists to support—and where gaps limit—the development of models that reflect extant conditions and offender decision-making. For the field to progress, as a minimum, standardized reporting that encourages transparency will be necessary

Mao with Smart Phones and Internet? A Comparison of Classic Guerrilla Warfare with Fourth and Fifth Generation Warfare Using an Agent-Based Model for Simulation

Fourth Generation Warfare (4GW) theory shares many characteristics of classical guerrilla warfare (CGW) theory in security studies literature. Proponents claim that 4GW is a revolution in war that overturns traditional measures of military power, while critics counter that 4GW is simply CGW in an updated context. Another group posits Fifth Generation Warfare (5GW), which adds additional information-age technologies and uses “any and all means,” (military and extra-military) to attack both the enemy’s will and capability to resist. The irregular subset of 5GW strategies appear to be an extension of 4GW with the addition of advanced information-age technologies: mobile phones and internet spreading propaganda instantly to friendly groups as well as national and trans-national enemies, while unconventional tactics such as suicide bombings and terrorist actions attempt to drain the will of opponents to continue the fight. The CGW and 4/5GW strategies are modeled in an agent-based simulation to evaluate similarities and differences in speed to victory, territory controlled, and the identity of the winning side. Emergent behaviors are compared with historical data. Fourth Generation Warfare (4GW) as conceptualized by numerous military scholars shares many characteristics of guerrilla tactics in the classical military literature of Sun Tzu, Wellington, Clausewitz, Mao, and Giap. Proponents of 4GW claim that its development has significantly altered the ratio of strength of industrialized and guerrilla forces, and thus the likelihood of weaker forces (as measured in previous military contexts) prevailing against forces assessed by traditional measures as stronger. Critics point to a lack of intellectual rigor in defining the salient characteristics of 4GW and charge that it is simply a re-statement of classical guerrilla war (CGW) tactics, albeit with improved communications and propaganda capabilities in a social media cultural context. This research models CGW and 4GW in conjunction with the irregular subset of 5GW in an agent-based simulation using NetLogo software (Wilensky, 1999) in order to explore differences in time and probability of victory and increased area of territory controlled by 4GW and irregular 5GW forces. These forces are then pitted against their respective industrial-age and information-age opponents. Emergent behaviors offer insights into the similarities and differences of CGW. The outputs are then compared to historical data to help answer the question of whether 4/5GW comprise a significant military revolution that threatens to upend traditional measures of military superiority, or they are merely an adaptation of old tactics to a new context. The results generally favored the rebels in both CGW and 4/5GW scenarios. Increasing Red Communications capability in the 4/5GW scenario overall increased Red Territory controlled as compared to the CGW scenario. However, increasing Blue Communications capability also increased Red Territory gained in both models. This could be interpreted that an overall increase in communications capabilities leads to more aggressive tactics and more engagements for both sides. Blue and Red communications in the 4/5GW scenarios are also associated with a decrease in both Red and Blue time to victory, indicating that the pace of engagements is accelerated in the 4/5GW scenarios. Finally, the model comparing identity of victor after 10 years produced mixed results. An increase in Red Communications was associated with a decrease in the log-odds of Blue Victory after 10 years in 4/5GW model, as expected. However, an increase Blue Communications also appeared to be associated with an increase in the log-odds of Red Victory in the 4/5GW model, a somewhat contradictory result. The addition of 21st century technologies seemed to change the overall dynamic compared to CGW only in specific cases, and usually only marginally. The research project was purposefully designed so that the 4/5GW capabilities would be additions to a basic model of guerrilla warfare. There is danger that these additions were simply insufficient in modeling the true extent of the differences between the two concepts of war, and that 4/5GW tactics are, in fact, revolutionary and not evolutionary. Further study is required to answer the question conclusively