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Abstract: Abstract The ground truth program used simulations as test beds for social science research methods. The simulations had known ground truth and were capable of producing large amounts of data. This allowed research teams to run experiments and ask questions of these simulations similar to social scientists studying real-world systems, and enabled robust evaluation of their causal inference, prediction, and prescription capabilities. We tested three hypotheses about research effectiveness using data from the ground truth program, specifically looking at the influence of complexity, causal understanding, and data collection on performance. We found some evidence that system complexity and causal understanding influenced research performance, but no evidence that data availability contributed. The ground truth program may be the first robust coupling of simulation test beds with an experimental framework capable of teasing out factors that determine the success of social science research. PubDate: 2022-04-30
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Abstract: Abstract Social systems are uniquely complex and difficult to study, but understanding them is vital to solving the world’s problems. The Ground Truth program developed a new way of testing the research methods that attempt to understand and leverage the Human Domain and its associated complexities. The program developed simulations of social systems as virtual world test beds. Not only were these simulations able to produce data on future states of the system under various circumstances and scenarios, but their causal ground truth was also explicitly known. Research teams studied these virtual worlds, facilitating deep validation of causal inference, prediction, and prescription methods. The Ground Truth program model provides a way to test and validate research methods to an extent previously impossible, and to study the intricacies and interactions of different components of research. PubDate: 2022-04-18
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Abstract: Abstract The objective of this paper is to examine the evolutionary mechanism regarding how a co-creation community network evolves as the growth of user interaction, which differs from the existing studies concentrating on the explanation of the forward problems of information management systems (e.g. motivational identification of user participation and examination of users’ outcomes). To achieve this objective, network generation model is formulated as nodes of users, ties of user’s interactions, random process, and preferential attachment. Then, real networks formulated by practice and artificial networks generated by the proposed model are compared by cumulative degree distribution, so as to validate the feasibility of the proposed model and to explain user behavior from the perspective of link formulation. Results indicate that: (i) new users account for main contributions for the development of co-creation community; (ii) new users prefer to interact high-influence all the time, while old users interchangeably choose preferential attachment or random linking in different time periods, (iii) the initial number of users, the probability for choosing preferential attachment or random attachment has a great influence on the properties of a user interactive network. PubDate: 2022-04-05
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract An agent-based model is proposed and tested. This model aims to simulate agency as conceptualized in Bandura's (Am Psychol 37:122–147, 1982; Organ Behav Hum Decis Process 50:248–287; Annu Rev Psychol 52: 1–26) Social cognitive theory. Social cognitive theory has been used to explain the continued underrepresentation of females in certain fields, most notably fields that are associated with engineering and technology. The theory proposes that agents acquire information from four different sources, and then, through a process of reciprocal interaction, these agents develop their perception of self-efficacy. In this study, an agent-based model is used to model this interaction. The output from the simulation supports the validity of the model used and illustrates how agency "emerges" from the triadic interaction. The model successfully simulates several of the theorized aspects of social cognitive theory. The simulation results reveal that even small gendered differences can lead to female misrepresentation in certain fields. The model also shows that female discouragement plays a larger role than male encouragement in female underrepresentation. The implications of these results are discussed. Finally, the limitations of the model are discussed, along with directions for future research. PubDate: 2022-03-01
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Abstract: Abstract 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. PubDate: 2022-03-01
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Abstract: Abstract Cyberbullying has become a global problem that victimizes social media users and threatens freedom of speech. Charged language against victims undermines the sharing of opinion in the absence of online oversight. Aggressive cyberbullies routinely patrol social media to identify victims, post abusive comments, and curtail public discourse. The victims typically suffer depression and may even attempt suicide. However, simply banning abusive words used by cyberbullies is not an effective response. This study examines the efficacy of using charged language-action cues as predictor variables to profile cyberbullying on Twitter. The study contributes to a proactive confirmation for computationally profiling cyberbullying based on charged language. Charged language-action cues can strongly profile cyberbullying activity with statistical significance and consistency. Big data profiling analytics based on charged language can prevent cyberbullies from possible criminal activity, protect potential victims, and provide a proactive measure to profile cyberbullying for mediation entities such as social media platforms, youth counselors and law enforcement agencies. PubDate: 2022-02-07 DOI: 10.1007/s10588-022-09360-5
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Abstract: Abstract Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions. PubDate: 2022-01-11 DOI: 10.1007/s10588-021-09358-5
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Abstract: Abstract A scientific model’s usefulness relies on its ability to explain phenomena, predict how such phenomena will be impacted by future interventions, and prescribe actions to achieve desired outcomes. We study methods for learning causal models that explain the behaviors of simulated “human” populations. Through the Ground Truth project, we solved a series of Challenges where our explanations, predictions and prescriptions were scored against ground truth information. We describe the processes that emerged for applying causal discovery, network analysis, agent-based modeling and other analytical methods to inform solutions to Challenge tasks. We present our team’s overall performance results on these Challenges and discuss implications for future efforts to validate social scientific research using simulation-based challenges. PubDate: 2022-01-10 DOI: 10.1007/s10588-021-09353-w
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Abstract: Abstract ACCESS—the Agent-based Causal simulator with Cognitive, Environmental, and Social System factors—is an agent-based simulation of an alternate world that is designed to test social science methodologies’ abilities to explain, predict, and prescribe policies for complex social systems. The ACCESS world model includes behaviors based on behavioral and cognitive sciences within and across individuals, groups, and the society to create a multi-level model that exhibits emergent phenomena. In this paper, we detail the logic underlying our conceptualization of the entities (individuals, groups, and the world) and their interactions. We also provide details on how we used the ACCESS model to challenge and score social scientist teams’ abilities to explain, predict, and prescribe in the artificial world as part of the DARPA Ground Truth program. PubDate: 2021-12-02 DOI: 10.1007/s10588-021-09352-x
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Abstract: Abstract It is well recognized that many organizations operate under situations of high complexity that arises from pervasive interdependencies between their decision elements. While prior work has discussed the benefits of low to moderate complexity, the literature on how to cope with high complexity is relatively sparse. In this study, we seek to demonstrate that Lindblom’s decision-making principle of muddling through is a very effective approach that organizations can use to cope with high complexity. Using a computational simulation (NK) model, we show that Lindblom’s muddling through approach obtains outcomes superior to those obtained from boundedly rational decision-making approaches when complexity is high. Moreover, our results also show that muddling through is an appropriate vehicle for bringing in radical organizational change or far-reaching adaptation. PubDate: 2021-11-28 DOI: 10.1007/s10588-021-09354-9
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Abstract: Abstract Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling. PubDate: 2021-11-18 DOI: 10.1007/s10588-021-09351-y
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Abstract: Abstract SCAMP (Social Causality using Agents with Multiple Perspectives) is one of four social simulators that generated socially realistic data for the Ground Truth program. Unlike the other three simulators, it is based on a computational principle, stigmergy, inspired by social insects. Using this approach, we modeled conflict in a nation-state inspired by the ongoing scenario in Syria. This paper summarizes stigmergy and describes the Conflict World we built in SCAMP. PubDate: 2021-11-16 DOI: 10.1007/s10588-021-09347-8
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Abstract: Abstract We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents’ daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model’s design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world. PubDate: 2021-11-07 DOI: 10.1007/s10588-021-09348-7
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Abstract: Abstract This paper develops a simple simulation model to study the relation between the nature of knowledge and the architecture of economic systems. The market and the firm are different mechanisms for coordinating economic activity in a system where knowledge is widely dispersed. While the market solves coordination problems by decentralizing decision-making, the firm solves coordination problems by centralizing knowledge. The market incurs the cost of finding potential exchange partners and agreeing on terms of trade, while the firm incurs the cost of centralizing dispersed knowledge. The market therefore has an advantage over the firm in coordinating activities in which knowledge is difficult to centralize. The nature of knowledge involved in an economic activity influences not only the choice of the institution through which it is coordinated but also the internal structure of the institution. More specifically, the more hierarchical the firm, the better it is at using changing knowledge, but the worse it is at using knowledge which is difficult to transfer from one individual to another. Therefore, the number of layers in the hierarchy of the firm is influenced by the rate at which knowledge changes relative to the difficulty associated with communicating it. PubDate: 2021-10-26 DOI: 10.1007/s10588-021-09350-z
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Abstract: Abstract In this paper, a multi-value cellular automata model under Lagrange coordinates is proposed based on reality, the traffic flow in the Lagrange coordinate is simulated on the basis of the evolution equation of the model. From the fundamental diagram of the results under various conditions, it found that the three commonly used parameters of traffic flow in simulation is consistent with the empirical data. Specifically, traffic density and the number of lanes have a significant impact on traffic flow. The lower the density is, the more lanes there are, and the greater the flow. The research of this paper can help to develop more advanced traffic research technology, and improve the efficiency of traffic work subsequently. Simultaneously, it will bring convenience to people and promote the development of green traffic. PubDate: 2021-10-19 DOI: 10.1007/s10588-021-09345-w
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Abstract: Abstract The main aim of this paper is to establish the hidden incidences that can improve specific aspects of beekeeper Small and Medium Enterprises (SME) innovation capabilities in Boyacá-Colombia. The methodology is focused on the use of the expertons model, adequacy coefficient and forgotten effects theory. A questionnaire of 58 items about different innovation management areas is distributed to 14 beekeeper SMEs in the area of Boyacá-Colombia. The findings suggest that there are two specific actions that can improve innovation culture. These actions are related to marketing strategies and product innovation. The innovation strategy has a significant influence on the development of an innovative culture in the beekeeper SMEs; one must have a clear orientation to the innovation objectives in the marketing strategy, which is transferred to the product. Likewise, the utility of fuzzy methods for analysis when information is limited, subjective and scarce is highlighted. Finally, this research can be useful to practitioners and academics because the findings can serve as guidelines to understanding sources and enablers of innovation. In addition, the findings show how fuzzy methods help in contending with incomplete and subjective information, highlighting the meaning of the information rather than its measurement. PubDate: 2021-06-19 DOI: 10.1007/s10588-020-09321-w
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Abstract: Abstract Competitiveness, defined as the rate of success in attracting and maintaining industries to foster the sustained improvement in citizens’ wellbeing, has been a long-pursued goal for regions and nations. Today’s rapid advancements in technology, especially in telecommunications, open challenges for decision and policy makers to generate effective and efficient solutions in a global scenario. In this context, the latest developments in artificial intelligence, machine learning and deep learning open new paths for describing, analyzing, and representing complex phenomena in systemic environments. This paper presents a model using a neural network to predict the behavior of competitive benchmarks using public expenditure variables. The theory of control, in which the neural network approach is based, offers some advantages such as solving the problem while considering the dynamic nature of the phenomenon and allowing control blocks to be implemented in a straightforward method. The present paper establishes a neural network model that links control, administration, and systems theories in a statistically sound approach that connects both sets of variables, opening the path for extensions that allow optimal allocation of resources. PubDate: 2021-06-16 DOI: 10.1007/s10588-021-09338-9
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Abstract: Abstract Fuzzy systems in innovation and sustainability are important topics in literature nowadays. A lot of new formulations in fuzzy systems are being made including interesting applications in different topics. The aim of this special issue is to present different works made in this line of research that were presented in the IV International Congress of Innovation and Sustainability (ICONIS). PubDate: 2021-06-11 DOI: 10.1007/s10588-021-09334-z
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Abstract: Abstract Information and communication technologies (ICT) has the ability to create value by enabling other firm capabilities. Based on the ICT-enabled capabilities perspective, this study explores the direct and indirect effects between lower- and higher-order capabilities, such as ICT, knowledge management capability (KM) and product innovation flexibility (PIF), on the performance of Ibero-American small- and medium-sized enterprises (SMEs). This paper uses second-order structural equation models to test the research hypotheses with a sample of 130 Ibero-American SMEs. The results contribute to filling the gap in the SME-focused literature on empirical studies examining ICT-enabled capabilities and firm performance. The results show an enabling effect of ICT on higher-order capabilities, such as KM and PIF, which, by acting as mediating variables, create value and improve performance through innovation in firms. PubDate: 2021-06-10 DOI: 10.1007/s10588-021-09333-0
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