@article{jayalath_gunaratne_rand_seneviratne_garibay_2023, title={A GENERALIZATION OF THRESHOLD-BASED AND PROBABILITY-BASED MODELS OF INFORMATION DIFFUSION}, volume={26}, ISSN={["1793-6802"]}, url={https://doi.org/10.1142/S0219525923500054}, DOI={10.1142/S0219525923500054}, abstractNote={ Diffusion of information through complex networks is of interest in studies such as propagation prediction and influence maximization, both of which have applications in viral marketing and rumor controlling. There are a variety of information diffusion models, all of which simulate the adoption and spread of information over time. However, there is a lack of understanding of whether, despite their conceptual differences, these models represent the same underlying generative structures. For instance, if two different models utilize different conceptual mechanisms, but generate the same results, does the choice of model matter? A classification of diffusion of information models is developed based on the neighbor knowledge of the model infection requirement and the stochasticity of the model. This classification allows for the identification of models that fall into each respective category. The study involves the analysis of the following agent-based models on directed scale-free networks: (1) a linear absolute threshold model (LATM), (2) a linear fractional threshold model (LTFM), (3) the independent cascade model (ICM), (4) Bass-Rand-Rust model (BRRM) (5) a stochastic linear absolute threshold model (SLATM) (6) a stochastic fractional threshold model (SLFTM), and (7) Dodds–Watts model (DWM). Through the execution of simulations and analysis of the experimental results, the distinctive properties of each model are identified. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application. }, number={02}, journal={ADVANCES IN COMPLEX SYSTEMS}, author={Jayalath, Chathura and Gunaratne, Chathika and Rand, William and Seneviratne, Chathurani and Garibay, Ivan}, year={2023}, month={Mar} }
 @article{weishampel_staicu_rand_2023, title={Classification of social media users with generalized functional data analysis}, volume={179}, ISSN={["1872-7352"]}, url={https://doi.org/10.1016/j.csda.2022.107647}, DOI={10.1016/j.csda.2022.107647}, abstractNote={Technological advancement has made possible the collection of data from social media platforms at unprecedented speed and volume. Current methods for analyzing such data lack interpretability, are computationally intensive, or require a rigid data specification. Functional data analysis enables the development of a flexible, yet interpretable, modeling framework to extract lower-dimensional relevant features of a user's posting behavior on social media, based on their posting activity over time. The extracted features can then be used to discriminate a malicious user from a genuine one. The proposed methodology can classify a binary time series in a computationally efficient manner and provides more insights into the posting behavior of social media agents. Performance of the method is illustrated numerically in simulation studies and on a motivating Twitter data set. The developed methods are applicable to other social media data, such as Facebook, Instagram, Reddit, or TikTok, or any form of digital interaction where the user's posting behavior is indicative of their user class.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Weishampel, Anthony and Staicu, Ana -Maria and Rand, William}, year={2023}, month={Mar} }
 @article{epstein_garibay_hatna_koehler_rand_2023, title={Special Section on "Inverse Generative Social Science": Guest Editors? Statement}, volume={26}, ISSN={["1460-7425"]}, DOI={10.18564/jasss.5085}, number={2}, journal={JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION}, author={Epstein, Joshua M. and Garibay, Ivan and Hatna, Erez and Koehler, Matthew and Rand, William}, year={2023}, month={Mar} }
 @misc{romero_chica_damas_rand_2023, title={Two decades of agent-based modeling in marketing: a bibliometric analysis}, volume={12}, ISSN={["2192-6360"]}, DOI={10.1007/s13748-023-00303-y}, number={3}, journal={PROGRESS IN ARTIFICIAL INTELLIGENCE}, author={Romero, Elena and Chica, Manuel and Damas, Sergio and Rand, William}, year={2023}, month={Sep}, pages={213–229} }
 @article{garibay_yousefi_aslett_baggio_hemberg_jayalath_mantzaris_miller_o'reilly_rand_et al._2022, title={Entropy-Based Characterization of Influence Pathways in Traditional and Social Media}, DOI={10.1109/CIC56439.2022.00016}, abstractNote={Despite much work on social media, analysis of individual influence campaigns, messages, and platforms, we lack the tools and techniques and fundamental research to effectively understand the information flows and their effects on the dynamics of the entire information ecosystem. For example, how information is amplified or dampened as it moves from one online community to another, how information is spinned or framed into narratives that favor or malign viewpoints organically or by foreign actors, how disinformation flows from fringe to mainstream communities, etc. We postulate that the information ecosystem is an attention economy, and that influence-the ability to gather attention towards a particular message or messages- is its currency. As a result, we model the information ecosystem as a complex network of influences flowing between actors, communities and platforms. This paper advocate for the use of information-theoretic entropic methods to model and characterize this complex network of influences over time: Influence Cascades Ecosystem (ICE). We envision leveraging the concept of influence cascades in conjunction with a novel geopolitical news-centric model of the information ecosystem in order to better understand the influence pathways by which various types of information (new articles from trusted, untrusted, fringe or mainstream sources) propagate across the social and traditional hybrid media environment.}, journal={2022 IEEE 8TH INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING, CIC}, author={Garibay, Ozlem Ozmen and Yousefi, Niloofar and Aslett, Kevin and Baggio, Jacopo and Hemberg, Erik and Jayalath, Chathura and Mantzaris, Alexander and Miller, Bruce and O'Reilly, Una-May and Rand, William and et al.}, year={2022}, pages={38–44} }
 @article{gunaratne_de_thakur_senevirathna_rand_smyth_lipscomb_2022, title={Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media}, volume={13558}, ISBN={["978-3-031-17113-0"]}, ISSN={["1611-3349"]}, url={https://publons.com/wos-op/publon/56056140/}, DOI={10.1007/978-3-031-17114-7_3}, abstractNote={Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.}, journal={SOCIAL, CULTURAL, AND BEHAVIORAL MODELING (SBP-BRIMS 2022)}, author={Gunaratne, Chathika and De, Debraj and Thakur, Gautam and Senevirathna, Chathurani and Rand, William and Smyth, Martin and Lipscomb, Monica}, year={2022}, pages={24–34} }
 @article{overgoor_rand_dolen_mazloom_2022, title={Simplicity is not key: Understanding firm-generated social media images and consumer liking}, volume={39}, ISSN={["1873-8001"]}, url={https://publons.com/wos-op/publon/56056139/}, DOI={10.1016/j.ijresmar.2021.12.005}, abstractNote={Social media platforms are becoming increasingly important marketing channels, and recently these channels are becoming dominated by content that is not textual, but visual in nature. In this paper, we explore the relationship between the visual complexity of firm-generated imagery (FGI) and consumer liking on social media. We use previously validated image mining methods, to automatically extract interpretable visual complexity measures from images. We construct a set of six interpretable measures that are categorized as either (1) feature complexity measures (i.e., unstructured pixel-level variation; color, luminance, and edges) or (2) design complexity measures (i.e., structured design-level variation; number of objects, irregularity of object arrangement, and asymmetry of object arrangement). These measures and their interpretability are validated using a human subject experiment. Subsequently, we relate these visual complexity measures to the number of likes. The results show an inverted u-shape between feature complexity and consumer liking and a regular u-shape relationship between design complexity and consumer liking. In addition, we demonstrate that using the six individual measures that constitute feature- and design complexity provides a more nuanced view of the relationship between the unique aspects of visual complexity and consumer liking of FGI on social media than observed in previous studies that used a more aggregated measure. Overall, the automated framework presented in this paper opens up a wide range of possibilities for studying the role of visual complexity in online content.}, number={3}, journal={INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING}, author={Overgoor, Gijs and Rand, William and Dolen, Willemijn and Mazloom, Masoud}, year={2022}, month={Sep}, pages={639–655} }
 @article{rand_stummer_2021, title={Agent-based modeling of new product market diffusion: an overview of strengths and criticisms}, volume={305}, ISSN={["1572-9338"]}, url={https://doi.org/10.1007/s10479-021-03944-1}, DOI={10.1007/s10479-021-03944-1}, abstractNote={Abstract}, number={1-2}, journal={ANNALS OF OPERATIONS RESEARCH}, publisher={Springer Science and Business Media LLC}, author={Rand, William and Stummer, Christian}, year={2021}, month={Oct}, pages={425–447} }
 @article{westmattelmann_grotenhermen_sprenger_rand_schewe_2021, title={Apart we ride together: The motivations behind users of mixed-reality sports}, volume={134}, ISSN={["1873-7978"]}, url={https://publons.com/wos-op/publon/52083075/}, DOI={10.1016/j.jbusres.2021.05.044}, abstractNote={A new form of sports platforms transfers traditional sports like cycling into a virtual world and lets users socialize, exercise or compete with each other. Despite the increasing public attention, there is no research on motivational factors of this advanced mixed-reality technology allowing virtual-mediated physical interaction. Therefore, we proposed a research model and tested it using structural equation modelling combined with qualitative interviews to investigate the platform’s usage. Our results reveal that utilitarian benefits relate to the task-purposes of health consciousness and training, while hedonic benefits relate to training, customizing and socializing. Hedonic benefits are more strongly related to use intention than utilitarian, but subgroup-specific differences are observed. Privacy concerns constitute a risk for all users to continued use of these platforms, while cheating is relevant only for competitive users. Use intention positively relates to actual use behavior in the form of usage time, number of races and followed users.}, journal={JOURNAL OF BUSINESS RESEARCH}, author={Westmattelmann, Daniel and Grotenhermen, Jan-Gerrit and Sprenger, Marius and Rand, William and Schewe, Gerhard}, year={2021}, month={Sep}, pages={316–328} }
 @article{gunaratne_rand_garibay_2021, title={Inferring mechanisms of response prioritization on social media under information overload}, volume={11}, ISSN={["2045-2322"]}, url={https://publons.com/wos-op/publon/37741987/}, DOI={10.1038/s41598-020-79897-5}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC REPORTS}, author={Gunaratne, Chathika and Rand, William and Garibay, Ivan}, year={2021}, month={Jan} }
 @article{senevirathna_gunaratne_rand_jayalath_garibay_2021, title={Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media}, volume={23}, ISSN={["1099-4300"]}, url={https://doi.org/10.3390/e23020160}, DOI={10.3390/e23020160}, abstractNote={Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.}, number={2}, journal={ENTROPY}, publisher={MDPI AG}, author={Senevirathna, Chathurani and Gunaratne, Chathika and Rand, William and Jayalath, Chathura and Garibay, Ivan}, year={2021}, month={Feb} }
 @article{rust_rand_huang_stephen_brooks_chabuk_2021, title={Real-Time Brand Reputation Tracking Using Social Media}, volume={85}, ISSN={["1547-7185"]}, url={https://publons.com/wos-op/publon/48024162/}, DOI={10.1177/0022242921995173}, abstractNote={ How can we know what stakeholders think and feel about brands in real time and over time? Most brand reputation measures are at the aggregate level (e.g., the Interbrand “Best Global Brands” list) or rely on customer brand perception surveys on a periodical basis (e.g., the Y&R Brand Asset Valuator). To answer this question, brand reputation measures must capture the voice of the stakeholders (not just ratings on brand attributes), reflect important brand events in real time, and connect to a brand’s financial value to the firm. This article develops a new social media–based brand reputation tracker by mining Twitter comments for the world’s top 100 brands using Rust–Zeithaml–Lemon’s value–brand–relationship framework, on a weekly, monthly, and quarterly basis. The article demonstrates that brand reputation can be monitored in real time and longitudinally, managed by leveraging the reciprocal and virtuous relationships between the drivers, and connected to firm financial performance. The resulting measures are housed in an online longitudinal database and may be accessed by brand reputation researchers. }, number={4}, journal={JOURNAL OF MARKETING}, author={Rust, Roland T. and Rand, William and Huang, Ming-Hui and Stephen, Andrew T. and Brooks, Gillian and Chabuk, Timur}, year={2021}, month={Jul}, pages={21–43} }
 @article{deep agent: studying the dynamics of information spread and evolution in social networks_2020, url={https://publons.com/wos-op/publon/48244600/}, journal={ArXiv}, year={2020} }
 @article{gopal_karmegam_koka_rand_2020, title={Is the Grass Greener? On the Strategic Implications of Moving Along the Value Chain for IT Service Providers}, volume={31}, ISSN={["1526-5536"]}, url={https://publons.com/wos-op/publon/36772765/}, DOI={10.1287/isre.2019.0879}, abstractNote={ Information technology (IT) service providers that offer both customized and routinized IT services (help-desk services, software services, business process outsourcing, consulting services) are often advised to consider moving up or down the value chain. Such advice is couched in language that emphasizes the revenue possibilities that exist higher up the value chain (moving from software outsourcing to packaged software, for instance) or volume that exists lower down in the value chain. However, the reality for firms that have attempted such moves is more ambiguous—many highly successful service providers have tried to move out of their niche and failed. Why? We address this question of why service providers may fail when they move up or down the value chain using agent-based modeling. We create a set of representative IT service firms, endow them with different types of resources and capabilities, and model the profitability implications of moves up and down the value chain in the presence of competitors. We run a series of simulations using these representative firms to see when such moves along the value chain are likely to be successful and when they are not. We find that firms moving up the value chain are successful only when such moves are accompanied by significant resource changes. In contrast, firms moving down the value chain are likely to be successful only if such moves are accompanied by learning capability arising out of higher absorptive capacity. Not surprisingly, we find that moves along with the value chain without significant investments in resources and capabilities, for the most part, end in failure. }, number={1}, journal={INFORMATION SYSTEMS RESEARCH}, author={Gopal, Anandasivam and Karmegam, Sabari Rajan and Koka, Balaji R. and Rand, William M.}, year={2020}, month={Mar}, pages={148–175} }
 @article{gunaratne_baral_rand_garibay_jayalath_senevirathna_2020, title={The effects of information overload on online conversation dynamics}, volume={26}, ISSN={["1572-9346"]}, url={https://publons.com/wos-op/publon/33087287/}, DOI={10.1007/s10588-020-09314-9}, abstractNote={Abstract}, number={2}, journal={COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY}, author={Gunaratne, Chathika and Baral, Nisha and Rand, William and Garibay, Ivan and Jayalath, Chathura and Senevirathna, Chathurani}, year={2020}, month={Jun}, pages={255–276} }
 @article{roxburgh_guan_shin_rand_managi_lovelace_meng_2019, title={Characterising climate change discourse on social media during extreme weather events}, volume={54}, ISSN={["1872-9495"]}, url={https://doi.org/10.1016/j.gloenvcha.2018.11.004}, DOI={10.1016/j.gloenvcha.2018.11.004}, abstractNote={When extreme weather events occur, people often turn to social media platforms to share information, opinions and experiences. One of the topics commonly discussed is the role climate change may or may not have played in influencing an event. Here, we examine Twitter posts that mentioned climate change in the context of three high-magnitude extreme weather events – Hurricane Irene, Hurricane Sandy and Snowstorm Jonas – in order to assess how the framing of the topic and the attention paid to it can vary between events. We also examine the role that contextual factors can play in shaping climate change coverage on the platform. We find that criticism of climate change denial dominated during Irene, while political and ideological struggle frames dominated during Sandy. Discourse during Jonas was, in contrast, more divided between posts about the scientific links between climate change and the events, and posts contesting climate science in general. The focus on political and ideological struggle frames during Sandy reflects the event’s occurrence at a time when the Occupy movement was active and the 2012 US Presidential Election was nearing. These factors, we suggest, could also contribute to climate change being a more prominent discussion point during Sandy than during Irene or Jonas. The Jonas frames, meanwhile, hint at lesser public understanding of how climate change may influence cold weather events when compared with tropical storms. Overall, our findings demonstrate how event characteristics and short-term socio-political context can play a critical role in determining the lenses through which climate change is viewed.}, journal={GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS}, publisher={Elsevier BV}, author={Roxburgh, Nicholas and Guan, Dabo and Shin, Kong Joo and Rand, William and Managi, Shunsuke and Lovelace, Robin and Meng, Jing}, year={2019}, month={Jan}, pages={50–60} }
 @article{burghardt_rand_girvan_2019, title={Inferring models of opinion dynamics from aggregated jury data}, volume={14}, ISSN={["1932-6203"]}, url={https://publons.com/wos-op/publon/42252607/}, DOI={10.1371/journal.pone.0218312}, abstractNote={Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant’s guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.}, number={7}, journal={PLOS ONE}, author={Burghardt, Keith and Rand, William and Girvan, Michelle}, year={2019}, month={Jul} }
 @article{overgoor_chica_rand_weishampel_2019, title={Letting the Computers Take Over: Using AI to Solve Marketing Problems}, volume={61}, ISSN={["2162-8564"]}, url={https://publons.com/wos-op/publon/37201971/}, DOI={10.1177/0008125619859318}, abstractNote={ Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications. }, number={4}, journal={CALIFORNIA MANAGEMENT REVIEW}, author={Overgoor, Gijs and Chica, Manuel and Rand, William and Weishampel, Anthony}, year={2019}, month={Aug}, pages={156–185} }
 @article{rand_rust_kim_2018, title={Complex systems: marketing’s new frontier}, volume={8}, url={https://doi.org/10.1007/s13162-018-0122-2}, DOI={10.1007/s13162-018-0122-2}, number={3-4}, journal={AMS Review}, publisher={Springer Science and Business Media LLC}, author={Rand, William and Rust, Roland T. and Kim, Min}, year={2018}, month={Dec}, pages={111–127} }
 @article{darmon_rand_girvan_2018, title={Computational landscape of user behavior on social media}, volume={98}, ISSN={["2470-0053"]}, url={https://publons.com/wos-op/publon/43964365/}, DOI={10.1103/PhysRevE.98.062306}, abstractNote={With the increasing abundance of 'digital footprints' left by human interactions in online environments, e.g., social media and app use, the ability to model complex human behavior has become increasingly possible. Many approaches have been proposed, however, most previous model frameworks are fairly restrictive. We introduce a new social modeling approach that enables the creation of models directly from data with minimal a priori restrictions on the model class. In particular, we infer the minimally complex, maximally predictive representation of an individual's behavior when viewed in isolation and as driven by a social input. We then apply this framework to a heterogeneous catalog of human behavior collected from fifteen thousand users on the microblogging platform Twitter. The models allow us to describe how a user processes their past behavior and their social inputs. Despite the diversity of observed user behavior, most models inferred fall into a small subclass of all possible finite-state processes. Thus, our work demonstrates that user behavior, while quite complex, belies simple underlying computational structures.}, number={6}, journal={PHYSICAL REVIEW E}, author={Darmon, David and Rand, William and Girvan, Michelle}, year={2018}, month={Dec} }
 @article{smith_rand_2018, title={Simulating Macro-Level Effects from Micro-Level Observations}, volume={64}, ISSN={["1526-5501"]}, url={https://doi.org/10.1287/mnsc.2017.2877}, DOI={10.1287/mnsc.2017.2877}, abstractNote={ We consider the fruits of integrating agent-based modeling (ABM) with lab-based experimental research with human subjects. While both ABM and lab experiments have similar aims—to identify the rules, tendencies, and heuristics by which individual agents make decisions and respond to external stimuli—they work toward their common goal in notably different ways. Behavioral-lab research typically exposes human subjects to experimental manipulations, or treatments, to make causal inferences by observing variation in response to the treatment. ABM researchers ascribe individual simulated “agents” with decision rules describing their behavior and subsequently attempt to replicate “macro” level empirical patterns. Integration of ABM and lab experiments presents advantages for both sets of researchers. ABM researchers will benefit from exposure to a larger set of empirically validated mechanisms that can add nuance and refinement to their models of human behavior and system dynamics. Lab-oriented researchers will gain from ABM a method for assessing the validity and magnitude of their findings, adjudicating between competing mechanisms, developing new theory to test in the lab, and exploring macro-level, long-run implications of subtle, micro-level observations that can be difficult to observe in the field. We offer an example of this mixed-method approach related to status, social networks, and job search and issue guidance for future research attempting such integration. }, number={11}, journal={MANAGEMENT SCIENCE}, publisher={Institute for Operations Research and the Management Sciences (INFORMS)}, author={Smith, Edward Bishop and Rand, William}, year={2018}, month={Nov}, pages={5405–5421} }
 @article{chica_rand_2017, title={Building Agent-Based Decision Support Systems for Word-of-Mouth Programs: A Freemium Application}, volume={54}, ISSN={["1547-7193"]}, url={https://publons.com/wos-op/publon/19540600/}, DOI={10.1509/jmr.15.0443}, abstractNote={Marketers must constantly decide how to implement word-of-mouth (WOM) programs, and a well-developed decision support system (DSS) can provide them valuable assistance in doing so. The authors propose an agent-based framework that aggregates social network–level individual interactions to guide the construction of a successful DSS for WOM. The framework presents a set of guidelines and recommendations to (1) involve stakeholders, (2) follow a data-driven iterative modeling approach, (3) increase validity through automated calibration, and (4) understand the DSS behavior. This framework is applied to build a DSS for a freemium app in which premium users discuss the product with their social network and promote its viral adoption. After its validation, the agent-based DSS forecasts the aggregate number of premium sales over time and the most likely users to become premium in the near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the number of premiums through targeting and implementing reward policies.}, number={5}, journal={JOURNAL OF MARKETING RESEARCH}, author={Chica, Manuel and Rand, William}, year={2017}, month={Oct}, pages={752–767} }
 @article{verhoef_stephen_kannan_luo_abhishek_andrews_bart_datta_fong_hoffman_et al._2017, title={Consumer Connectivity in a Complex, Technology-enabled, and Mobile-oriented World with Smart Products}, volume={40}, ISSN={["1520-6653"]}, url={https://publons.com/wos-op/publon/12108768/}, DOI={10.1016/j.intmar.2017.06.001}, abstractNote={ Today's consumers are immersed in a vast and complex array of networks. Each network features an interconnected mesh of people and firms, and now, with the rise of the Internet of Things (IoT), also objects. Technology (particularly mobile devices) enables such connections, and facilitates many kinds of interactions in these networks—from transactions, to social information sharing, to people interfacing with connected devices (e.g., wearable technology). }, journal={JOURNAL OF INTERACTIVE MARKETING}, author={Verhoef, Peter C. and Stephen, Andrew T. and Kannan, P. K. and Luo, Xueming and Abhishek, Vibhanshu and Andrews, Michelle and Bart, Yakov and Datta, Hannes and Fong, Nathan and Hoffman, Donna L. and et al.}, year={2017}, month={Nov}, pages={1–8} }
 @article{burghardt_alsina_girvan_rand_lerman_2017, title={The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange}, volume={12}, ISSN={["1932-6203"]}, url={https://publons.com/wos-op/publon/6038849/}, DOI={10.1371/journal.pone.0173610}, abstractNote={Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the "wisdom of crowds" effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer's salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend on heuristics to a greater extent than voters when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic, and they are more likely to choose the answer after it has been accepted than before that answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers to a question increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grows.}, number={3}, journal={PLOS ONE}, author={Burghardt, Keith and Alsina, Emanuel F. and Girvan, Michelle and Rand, William and Lerman, Kristina}, year={2017}, month={Mar} }
 @article{brand buzz in the echoverse_2016, url={https://publons.com/wos-op/publon/19798607/}, DOI={10.1509/JM.15.0033}, abstractNote={ Social media sites have created a reverberating “echoverse” for brand communication, forming complex feedback loops (“echoes”) between the “universe” of corporate communications, news media, and user-generated social media. To understand these feedback loops, the authors process longitudinal, unstructured data using computational linguistics techniques and analyze them using econometric methods. By assembling one of the most comprehensive data sets in the brand communications literature with corporate communications, news stories, social media, and business outcomes, the authors document the echoverse (i.e., feedback loops between all of these sources). Furthermore, the echoverse has changed as online word of mouth has become prevalent. Over time, online word of mouth has fallen into a negativity spiral, with negative messages leading to greater volume, and firms are adjusting their communications strategies in response. The nature of brand communications has been transformed by online technology as corporate communications move increasingly from one to many (e.g., advertising) to one to one (e.g., Twitter) while consumer word of mouth moves from one to one (e.g., conversations) to one to many (e.g., social media). The results indicate that companies benefit from using social media (e.g., Twitter) for personalized customer responses, although there is still a role for traditional brand communications (e.g., press releases, advertising). The evolving echoverse requires managers to rethink brand communication strategies, with online communications becoming increasingly central. }, journal={Journal of Marketing}, year={2016} }
 @inproceedings{breaking into new data-spaces: infrastructure for open community science_2016, url={https://publons.com/wos-op/publon/16886924/}, DOI={10.1145/2818052.2855512}, abstractNote={Despite being freely accessible, open online community data can be difficult to use effectively. To access and analyze large amounts of data, researchers must become familiar with the meaning of data values. Then they must also find a way to obtain and process the datasets to extract their desired vectors of behavior and content. This process is fraught with problems that are solved over and over again by each research team/lab that breaks into a new dataset. Those who lack the necessary technical skills may never be able to start.}, booktitle={ACM Conference on Computer Supported Cooperative Work and Social Computing}, year={2016} }
 @article{competing opinions and stubborness: connecting models to data_2016, url={https://publons.com/wos-op/publon/6038865/}, DOI={10.1103/PHYSREVE.93.032305}, abstractNote={We introduce a general contagionlike model for competing opinions that includes dynamic resistance to alternative opinions. We show that this model can describe candidate vote distributions, spatial vote correlations, and a slow approach to opinion consensus with sensible parameter values. These empirical properties of large group dynamics, previously understood using distinct models, may be different aspects of human behavior that can be captured by a more unified model, such as the one introduced in this paper.}, journal={Physical Review E}, year={2016} }
 @article{yoo_rand_eftekhar_rabinovich_2016, title={Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises}, volume={45}, ISSN={["1873-1317"]}, url={https://publons.com/wos-op/publon/42282239/}, DOI={10.1016/j.jom.2016.05.007}, abstractNote={Abstract}, journal={JOURNAL OF OPERATIONS MANAGEMENT}, author={Yoo, Eunae and Rand, William and Eftekhar, Mahyar and Rabinovich, Elliot}, year={2016}, month={Jul}, pages={123–133} }
 @article{the simple rules of a complex world: william rand and roland rust_2016, url={https://publons.com/wos-op/publon/56056142/}, DOI={10.1108/EJM-02-2016-0109}, abstractNote={
Purpose
The purpose of this commentary is to explain that it is not useful to unnecessarily complicate a model. Striving for realism for its own sake does not advance understanding; however, making sure that a model provides valid insights is a useful goal.
}, journal={European Journal of Marketing}, year={2016} }
 @article{hsu_weng_cui_rand_2016, title={Understanding the complexity of project team member selection through agent-based modeling}, volume={34}, ISSN={0263-7863}, url={http://dx.doi.org/10.1016/J.IJPROMAN.2015.10.001}, DOI={10.1016/J.IJPROMAN.2015.10.001}, abstractNote={Previous research has recognized the significance of a team's work capacity and suggested the selection of team members based on individual skills and performance in alignment with task characteristics. However, work teams are complex systems with interdependence between workers and the social environment, and exhibit surprising, nonlinear behavior. This study utilizes Agent-Based Modeling (ABM) to understand the complexity of project team member selection and to examine how the functional diversity of teams and worker interdependence affect team performance in different economic conditions. Data for model validation was collected from 116 construction projects for the period from 2009 to 2011. The results show that teams with higher functional diversity can enhance the overall firm performance when the economy is in a downturn. This study suggests managers using knowledge of worker interdependence to protect higher-performing workers by minimizing disruption of interdependence in team member selection for improving firm performance.}, number={1}, journal={International Journal of Project Management}, publisher={Elsevier BV}, author={Hsu, Shu-Chien and Weng, Kai-Wei and Cui, Qingbin and Rand, William}, year={2016}, month={Jan}, pages={82–93} }
 @article{an agent-based model of urgent diffusion in social media_2015, url={https://publons.com/wos-op/publon/56056143/}, journal={Journal of Artificial Societies and Social Simulation}, year={2015} }
 @inproceedings{forecasting high tide: predicting times of elevated activity in online social media_2015, url={https://publons.com/wos-op/publon/56056145/}, DOI={10.1145/2808797.2809392}, abstractNote={Social media provides a powerful platform for influencers to broadcast content to a large audience of followers. In order to reach the greatest number of users, an important first step is to identify times when a large portion of a target population is active on social media, which requires modeling the behavior of those individuals. We propose three methods for behavior modeling: a simple seasonality approach based on time-of-day and day-of-week, an autoregressive approach based on aggregate fluctuations from seasonality, and an aggregation-of-individuals approach based on modeling the behavior of individual users. We test these methods on data collected from a set of users on Twitter in 2011 and 2012. We find that the performance of the methods at predicting times of high activity depends strongly on the tradeoff between true and false positives, with no method dominating. Our results highlight the challenges and opportunities involved in modeling complex social systems, and demonstrate how influencers interested in forecasting potential user engagement can use complexity modeling to make better decisions.}, booktitle={IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year={2015} }
 @article{the future applications of agent-based modeling in marketing_2014, url={https://publons.com/wos-op/publon/30937609/}, DOI={10.4324/9780203103036.CH20}, abstractNote={Agent-based modeling (ABM) gives researchers the ability to model, at a natural level, large markets with many interacting components. The agents within ABM can exist at multiple levels, be heterogeneous in both properties and actions, and can even adapt their actions over time. This provides a powerful ability to represent real-world business phenomenon at a rich level of detail. Within marketing, ABM has already been successfully used to model the diffusion of information and adoption of products, but there are many other areas of interest to marketing researchers that could benefit from an ABM approach. For instance, retail location decisions, inter-firm relationships/strategy/competition, marketing mix models, and servicescape design could all benefit from being analyzed using ABM. In this chapter, we explore this method, potential future applications within marketing, and explore future research questions in the context of this methodology.}, journal={Routledge Companion to the Future of Marketing}, year={2014} }
 @inbook{stonedahl_rand_2014, title={When Does Simulated Data Match Real Data?}, ISBN={9784431548461 9784431548478}, url={http://dx.doi.org/10.1007/978-4-431-54847-8_19}, DOI={10.1007/978-4-431-54847-8_19}, abstractNote={Agent-based models can be calibrated to replicate real-world data sets, but choosing the best set of parameters to achieve this result can be difficult. To validate a model, the real-world data set is often divided into a training and a test set. The training set is used to calibrate the parameters, and the test set is used to determine if the calibrated model represents the real-world data. The difference between the real-world data and the simulated data is determined using an error measure. When using evolutionary computation to choose the parameters, this error measure becomes the fitness function, and choosing the appropriate measure becomes even more crucial for a successful calibration process. We survey the effect of five different error measures in the context of a toy problem and a real-world problem (simulating online news consumption). We use each error measure in turn to calibrate on the training data set, and then examine the results of all five error measures on both the training and test data sets. For the toy problem, one measure was the Pareto-dominant choice for calibration, but no error measure dominated all the others for the real-world problem. Additionally, we observe the counterintuitive result that calibrating using one measure may sometimes lead to better performance on a second measure than could be achieved by calibrating using that second measure directly.}, booktitle={Advances in Computational Social Science}, publisher={Springer Japan}, author={Stonedahl, Forrest and Rand, William}, year={2014}, pages={297–313} }
 @article{automatic crowdsourcing-based classification of marketing messaging on twitter_2013, url={https://publons.com/wos-op/publon/33055720/}, DOI={10.1109/SOCIALCOM.2013.155}, abstractNote={As the volume of social media communications grow, many different stakeholders have sought to apply tools and methods for automatic identification of sentiment and topic in social network communications. In the domain of social media marketing it would be useful to automatically classify social media messaging into the classic framework of informative, persuasive and transformative advertising. In this paper we develop and present the construction and evaluation of supervised machine-learning classifiers for these concepts, drawing upon established procedures from the domains of sentiment analysis and crowd sourced text classification. We demonstrate that a reasonably effective classifier can be created to identify the informative nature of Tweets based on crowd sourced training data, we also present results for identifying persuasive and transformative content. We finish by summarizing our findings regarding applying these methods and by discussing recommendations for future work in the area of classifying the marketing content of Tweets.}, journal={ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM)}, year={2013} }
 @article{does love change on twitter? the dynamics of topical conversations in microblogging_2013, url={https://publons.com/wos-op/publon/56056144/}, DOI={10.1109/SOCIALCOM.2013.19}, abstractNote={Discovering automatically what people are talking about on social media with respect to a particular topic would be useful since it would give insight into how people perceive different topics. However, identifying trending terms words within a topical conversation is a difficult task. We take an information retrieval approach and use tf-idf to identify words that are more frequent in a focal conversation compared to other conversations on Twitter. This requires a query set of tweets on a particular topic (used for term frequency) and a control set of conversations to use for comparison (used for inverse document frequency). The terms identified as trending within a topical conversation are greatly affected by the particular control set used. There is no clear metric for whether one control set is better than another, since that is determined by the needs of the user, but we can investigate the stability properties of topics given different control sets. We propose a method for doing this, and show that some topics of conversation are more stable than other topics, and that this stability is also affected by whether only the most frequent terms are of interest (top-50), or if all words (full-vocabulary) are being examined. We end with a set of guidelines for how to build better topic analysis tools based on these results.}, journal={ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM)}, year={2013} }
 @article{improving prelaunch diffusion forecasts: using synthetic networks as simulated priors_2013, url={https://publons.com/wos-op/publon/33055719/}, DOI={10.1509/JMR.11.0508}, abstractNote={ Although the role of social networks and consumer interactions in new product diffusion is widely acknowledged, such networks and interactions are often unobservable to researchers. What may be observable, instead, are aggregate diffusion patterns for past products adopted within a particular social network. The authors propose an approach for identifying systematic conditions that are stable across diffusions and thus are “transferrable” to new product introductions within a given network. Using Facebook applications data, the authors show that incorporation of such systematic conditions improves prelaunch forecasts. This research bridges the gap between the disciplines of Bayesian statistics and agent-based modeling by demonstrating how researchers can use stochastic relationships simulated within complex systems as meaningful inputs for Bayesian inference models. }, journal={Journal of Marketing Research}, year={2013} }
 @article{media, aggregators, and the link economy: strategic hyperlink formation in content networks_2013, url={https://publons.com/wos-op/publon/3748488/}, DOI={10.1287/MNSC.2013.1710}, abstractNote={ A defining property of the World Wide Web is a content site's ability to place virtually costless hyperlinks to third-party content as a substitute or complement to its own content. Costless hyperlinking has enabled new types of players, usually referred to as content aggregators, to successfully enter content ecosystems, attracting traffic and revenue by hosting links to the content of others. This, in turn, has sparked a heated controversy between content creators and aggregators regarding the legitimacy and costs/benefits of uninhibited free linking. To our knowledge, this work is the first to model the complex interplay between content and links in settings where a set of sites compete for traffic. We develop a series of analytical models that distill how hyperlinking affects the (a) incentives of content nodes to produce quality content versus link to third-party content, (b) profits of the various stakeholders, (c) average quality of content that becomes available to consumers, and (d) impact of content aggregators. Our results provide a nuanced view of the so-called link economy, highlighting both the beneficial consequences and the drawbacks of free hyperlinks for content creators and consumers. }, journal={Management Science}, year={2013} }
 @article{predictability of user behavior in social media: bottom-up v. top-down modeling_2013, url={https://publons.com/wos-op/publon/56056146/}, DOI={10.1109/SOCIALCOM.2013.22}, abstractNote={Recent work has attempted to capture the behavior of users on social media by modeling them as computational units processing information. We propose to extend this perspective by explicitly examining the predictive power of such a view. We consider a network of fifteen thousand users on Twitter over a seven week period. To evaluate the predictability of the users, we apply two contrasting modeling paradigms: computational mechanics and echo state networks. Computational mechanics seeks to construct the simplest model with the maximal predictive capability, while echo state networks relax from very complicated dynamics until predictive capability is reached. We demonstrate that the behavior of users on Twitter can be well-modeled as processes with self-feedback and compare the performance of models built with both the statistical and neural paradigms.}, journal={ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM)}, year={2013} }
 @inproceedings{switching behavior in online auctions: empirical observations and predictive implications_2013, url={https://publons.com/wos-op/publon/56056141/}, booktitle={Winter Simulation Conference}, year={2013} }
 @article{agent-based modeling in marketing: guidelines for rigor_2011, url={http://dx.doi.org/10.1016/j.ijresmar.2011.04.002}, DOI={10.1016/j.ijresmar.2011.04.002}, abstractNote={Agent-based modeling can illuminate how complex marketing phenomena emerge from simple decision rules. Marketing phenomena that are too complex for conventional analytical or empirical approaches can often be modeled using this approach. Agent-based modeling investigates aggregate phenomena by simulating the behavior of individual “agents,” such as consumers or organizations. Some useful examples of agent-based modeling have been published in marketing journals, but widespread acceptance of the agent-based modeling method and publication of this method in the highest-level marketing journals have been slowed by the lack of widely accepted standards of how to do agent-based modeling rigorously. We address this need by proposing guidelines for rigorous agent-based modeling. We demonstrate these guidelines, and the value of agent-based modeling for marketing research, through the use of an example. We use an agent-based modeling approach to replicate the Bass model of the diffusion of innovations, illustrating the use of the proposed guidelines to ensure the rigor of the analysis. We also show how extensions of the Bass model that would be difficult to carry out using traditional marketing research techniques are possible to implement using a rigorous agent-based approach.}, journal={International Journal of Research in Marketing}, year={2011}, month={Sep} }
 @article{the shaky ladder hyperplane-defined functions and classic dynamic problems_2010, url={https://publons.com/wos-op/publon/56056149/}, DOI={10.1142/S1469026810002756}, abstractNote={ To improve the understanding of the GA in dynamic environments we explore a set of test problems, the shaky ladder hyper-defined functions (sl-hdf), and extend these functions to create versions that are equivalent to many classical dynamic problems. We do this by constraining the space of all sl-hdfs to create representations of these classical functions. We have examined three classical problems, and compared sl-hdf versions of these problems with their standard representations. These results show that the sl-hdfs are representative of a larger class of problems, and can represent a larger class of test suite. Previous results on sl-hdf showed that GA performance is best in the Defined Cliffs variant of the sl-hdf. We build upon these results to improve GA performance in several classes of real world dynamic problems by modifying the problem representation. These results lend insight into dynamic problems where the GA will perform well. }, journal={International Journal of Computational Intelligence and Applications}, year={2010} }
 @article{zellner_riolo_rand_brown_page_fernandez_2010, title={The Problem with Zoning: Nonlinear Effects of Interactions between Location Preferences and Externalities on Land Use and Utility}, volume={37}, ISSN={0265-8135 1472-3417}, url={http://dx.doi.org/10.1068/b35053}, DOI={10.1068/b35053}, abstractNote={ An important debate in the literature on exurban sprawl is whether low-density development results from residential demand, as operationalized by developers, or from exclusionary zoning policies. Central to this debate is the purpose of zoning, which could alternatively be a mechanism to increase the utility of residents by separating land uses and reducing spillover effects of development, or an obstacle to market mechanisms that would otherwise allow the realization of residential preferences. To shed light on this debate, we developed an agent-based model of land-use change to study how the combined effects of zoning-enforcement levels, density preferences, preference heterogeneity, and negative externalities from development affect exurban development and the utility of residents. Our computational experiments show that sprawl is not inevitable, even when most of the population prefers low densities. The presence of negative externalities consistently encourage sprawl while decreasing average utility and flattening the utility distribution. Zoning can reduce sprawl by concentrating development in specific areas, but in doing so decreases average utility and increases inequality. Zoning does not internalize externalities; instead, it contains externalities in areas of different development density so that residents bear the burden of the external effects of the density they prefer. Effects vary with residential preference distributions and levels of zoning enforcement. These initial investigations can help inform policy makers about the conditions under which zoning enforcement is preferable to free-market development and vice versa. Future work will focus on the environmental impacts of different settlement patterns and the role land-use and market-based policies play in this relationship. }, number={3}, journal={Environment and Planning B: Planning and Design}, publisher={SAGE Publications}, author={Zellner, Moira L and Riolo, Rick L and Rand, William and Brown, Daniel G and Page, Scott E and Fernandez, Luis E}, year={2010}, month={Jun}, pages={408–428} }
 @inproceedings{participatory simulation as a tool for agent-based simulation_2009, url={https://publons.com/wos-op/publon/56056148/}, booktitle={International Conference on Agents and Artificial Intelligence (ICAART)}, year={2009} }
 @article{zellner_page_rand_brown_robinson_nassauer_low_2009, title={The emergence of zoning policy games in exurban jurisdictions: Informing collective action theory}, volume={26}, ISSN={0264-8377}, url={http://dx.doi.org/10.1016/j.landusepol.2008.04.004}, DOI={10.1016/j.landusepol.2008.04.004}, abstractNote={Theoretical urban policy literature predicts the likelihood of free riding in the management of common goods such as forested open space; such outcome is often characterized as a Prisoner's Dilemma game. Numerous cases exist in which neighboring jurisdictions cooperate to maintain public goods, challenging the expected results, yet theoretical explanations of these cases have not been fully developed. In this paper, we use an agent-based model to explore how underlying micro-behaviors affect the payoffs obtained by two neighboring municipalities in a hypothetical exurban area. Payoffs are measured in terms of regional forested space and of local tax revenue at the end of the agent-based simulations; the municipalities affect these payoffs through their choice of residential zoning policies and the spillover effect between the neighboring jurisdictions. Zoning restrictions influence the conversion of farmland into residential subdivisions of different types, and consequently the location of heterogeneous residential households in the region. Developers and residents respond to the changing landscape characteristics, thus establishing a feedback between early and future land-use patterns. The structure of the simulated payoffs is analyzed using standard game theory. Our analysis shows that a variety of games, in addition to Prisoner's Dilemma, can emerge between the neighboring jurisdictions. Other games encourage coordination or subsidization, offering some explanations for the unexpected observations. The game realized in any given context depends on the initial characteristics of the landscape, the value given to the objectives each township seeks to maximize, and the income distribution of the population.}, number={2}, journal={Land Use Policy}, publisher={Elsevier BV}, author={Zellner, Moira L. and Page, Scott E. and Rand, William and Brown, Daniel G. and Robinson, Derek T. and Nassauer, Joan and Low, Bobbi}, year={2009}, month={Apr}, pages={356–367} }
 @article{brown_robinson_an_nassauer_zellner_rand_riolo_page_low_wang_2008, title={Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system}, volume={39}, ISSN={0016-7185}, url={http://dx.doi.org/10.1016/j.geoforum.2007.02.010}, DOI={10.1016/j.geoforum.2007.02.010}, abstractNote={We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns.}, number={2}, journal={Geoforum}, publisher={Elsevier BV}, author={Brown, Daniel G. and Robinson, Derek T. and An, Li and Nassauer, Joan I. and Zellner, Moira and Rand, William and Riolo, Rick and Page, Scott E. and Low, Bobbi and Wang, Zhifang}, year={2008}, month={Mar}, pages={805–818} }
 @article{wang_dam_yildirim_rand_wilensky_houk_2008, title={Reciprocity between the cerebellum and the cerebral cortex: Nonlinear dynamics in microscopic modules for generating voluntary motor commands}, volume={14}, ISSN={1076-2787 1099-0526}, url={http://dx.doi.org/10.1002/cplx.20241}, DOI={10.1002/cplx.20241}, abstractNote={Abstract}, number={2}, journal={Complexity}, publisher={Wiley}, author={Wang, Jun and Dam, Gregory and Yildirim, Sule and Rand, William and Wilensky, Uri and Houk, James C.}, year={2008}, month={Nov}, pages={29–45} }
 @inbook{alharbi_rand_riolo_2007, title={Understanding the Semantics of the Genetic Algorithm in Dynamic Environments}, ISBN={9783540718048 9783540718055}, url={http://dx.doi.org/10.1007/978-3-540-71805-5_72}, DOI={10.1007/978-3-540-71805-5_72}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Alharbi, Abir and Rand, William and Riolo, Rick}, year={2007}, month={Jun}, pages={657–667} }
 @inbook{rand_riolo_2006, title={The Effect of Building Block Construction on the Behavior of the GA in Dynamic Environments: A Case Study Using the Shaky Ladder Hyperplane-Defined Functions}, ISBN={9783540332374 9783540332381}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/11732242_75}, DOI={10.1007/11732242_75}, abstractNote={The shaky ladder hyperplane-defined functions (sl-hdf’s) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. We present three ways of constructing the sl-hdf’s by manipulating the way building blocks are constructed, combined, and changed. We examine the effect of the length of elementary building blocks used to create higher building blocks, and the way in which those building blocks are combined. We show that the effects of building block construction on the behavior of the GA are complex. Our results suggest that construction routines which increase the roughness of the changes in the environment allow the GA to perform better by preventing premature convergence. Moreover, short length elementary building blocks permit early rapid progress.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Rand, William and Riolo, Rick}, year={2006}, pages={776–787} }
 @article{path dependence and the validation of agent‐based spatial models of land use_2005, url={http://dx.doi.org/10.1080/13658810410001713399}, DOI={10.1080/13658810410001713399}, abstractNote={In this paper, we identify two distinct notions of accuracy of land‐use models and highlight a tension between them. A model can have predictive accuracy: its predicted land‐use pattern can be highly correlated with the actual land‐use pattern. A model can also have process accuracy: the process by which locations or land‐use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land‐use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent‐based land‐use model and using multi‐temporal land‐use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.}, journal={International Journal of Geographical Information Science}, year={2005}, month={Feb} }
 @inbook{rand_riolo_2005, title={Shaky Ladders, Hyperplane-Defined Functions and Genetic Algorithms: Systematic Controlled Observation in Dynamic Environments}, ISBN={9783540253969 9783540320036}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-32003-6_63}, DOI={10.1007/978-3-540-32003-6_63}, abstractNote={Though recently there has been interest in examining genetic algorithms (GAs) in dynamic environments, work still needs to be done in investigating the fundamental behavior of these algorithms in changing environments. When researching the GA in static environments, it has been useful to use test suites of functions that are designed for the GA so that the performance can be observed under systematic controlled conditions. One example of these suites is the hyperplane-defined functions (hdfs) designed by Holland [1]. We have created an extension of these functions, specifically designed for dynamic environments, which we call the shaky ladder functions. In this paper, we examine the qualities of this suite that facilitate its use in examining the GA in dynamic environments, describe the construction of these functions and present some preliminary results of a GA operating on these functions.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Rand, William and Riolo, Rick}, year={2005}, pages={600–609} }
 @article{brown_riolo_robinson_north_rand_2005, title={Spatial process and data models: Toward integration of agent-based models and GIS}, volume={7}, ISSN={1435-5930 1435-5949}, url={http://dx.doi.org/10.1007/S10109-005-0148-5}, DOI={10.1007/S10109-005-0148-5}, abstractNote={The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.}, number={1}, journal={Journal of Geographical Systems}, publisher={Springer Science and Business Media LLC}, author={Brown, Daniel G. and Riolo, Rick and Robinson, Derek T. and North, Michael and Rand, William}, year={2005}, month={May}, pages={25–47} }
 @inproceedings{the problem with a self-adaptative mutation rate in some environments - a case study using the shaky ladder hyperplane-defined functions_2005, url={https://publons.com/wos-op/publon/56056147/}, booktitle={Genetic and Evolutionary Computation Conference}, year={2005} }
 @article{brown_page_riolo_rand_2004, title={Agent-based and analytical modeling to evaluate the effectiveness of greenbelts}, volume={19}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2003.11.012}, DOI={10.1016/j.envsoft.2003.11.012}, abstractNote={We present several models of residential development at the rural–urban fringe to evaluate the effectiveness of a greenbelt located beside a developed area, for delaying development outside the greenbelt. First, we develop a mathematical model, under two assumptions about the distributions of service centers, that represents the trade-off between greenbelt placement and width, their effects on the rate of development beyond the greenbelt, and how these interact with spatial patterns of aesthetic quality and the locations of services. Next, we present three agent-based models (ABMs) that include agents with the potential for heterogeneous preferences and a landscape with the potential for heterogeneous attributes. Results from experiments run with a one-dimensional ABM agree with the starkest of the results from the mathematical model, strengthening the support for both models. Further, we present two different two-dimensional ABMs and conduct a series of experiments to supplement our mathematical analysis. These include examining the effects of heterogeneous agent preferences, multiple landscape patterns, incomplete or imperfect information available to agents, and a positive aesthetic quality impact of the greenbelt on neighboring locations. These results suggest how width and location of the greenbelt could help determine the effectiveness of greenbelts for slowing sprawl, but that these relationships are sensitive to the patterns of landscape aesthetic quality and assumptions about service center locations.}, number={12}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Brown, D and Page, S and Riolo, R and Rand, W}, year={2004}, month={Dec}, pages={1097–1109} }