Connecting strategy and system dynamics: an example and lessons learned

This article is based on my talk at the 2015 International System Dynamics Conference upon receiving the Jay W. Forrester Award for the article “Impact of growth opportunities and competition on firm-level capability development tradeoffs” (Organization Science 2012; 23(1): 138–154). It summarizes how that research connects strategy concepts with system dynamics (SD) modeling to inform the pressures managers face to focus on the short term as a result of endogenous growth opportunities and competition. Drawing on this example I discuss some potentially useful research tools and assumptions. I close by sharing personal reflections on the process of writing for non-SD academics and why I think making those connections is worthwhile

Human Growth and Body Weight Dynamics: An Integrative Systems Model

Quantifying human weight and height dynamics due to growth, aging, and energy balance can inform clinical practice and policy analysis. This paper presents the first mechanism-based model spanning full individual life and capturing changes in body weight, composition and height. Integrating previous empirical and modeling findings and validated against several additional empirical studies, the model replicates key trends in human growth including A) Changes in energy requirements from birth to old ages. B) Short and long-term dynamics of body weight and composition. C) Stunted growth with chronic malnutrition and potential for catch up growth. From obesity policy analysis to treating malnutrition and tracking growth trajectories, the model can address diverse policy questions. For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%–24% in energy intake which will be a source of significant inertia in obesity trends. In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages. Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.National Institutes of Health (U.S.). Office of Behavioral and Social Sciences Research (Contract HHSN276201000004C)National Institutes of Health (U.S.). Office of Behavioral and Social Sciences Research (Contract 1R21HL113680-01)

Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models

When is it better to use agent based (AB) models, and when should differential equation (DE) models be used? Where DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity in agent attributes and in the network of interactions among them. Using contagious disease as an example, we contrast the dynamics of AB models with those of the corresponding mean-field DE model, specifically, comparing the standard SEIR model-a nonlinear DE-to an explicit AB model of the same system. We examine both agent heterogeneity and the impact of different network structures, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Surprisingly, in many conditions the AB and DE dynamics are quite similar. Differences between the DE and AB models are not statistically significant on key metrics relevant to public health, including diffusion speed, peak load on health services infrastructure and total disease burden. We explore the conditions under which the AB and DE dynamics differ, and consider implications for managing infectious disease. The results extend beyond epidemiology: from innovation adoption to the spread of rumor and riot to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion

Reporting guidelines for simulation-based research in social sciences

Reproducibility of research is critical for the healthy growth and accumulation of reliable knowledge, and simulation-based research is no exception. However, studies show many simulation-based studies in the social sciences are not reproducible. Better standards for documenting simulation models and reporting results are needed to enhance the reproducibility of simulation-based research in the social sciences. We provide an initial set of Reporting Guidelines for Simulation-based Research (RGSR) in the social sciences, with a focus on common scenarios in system dynamics research. We discuss these guidelines separately for reporting models, reporting simulation experiments, and reporting optimization results. The guidelines are further divided into minimum and preferred requirements, distinguishing between factors that are indispensable for reproduction of research and those that enhance transparency. We also provide a few guidelines for improved visualization of research to reduce the costs of reproduction. Suggestions for enhancing the adoption of these guidelines are discussed at the end

Capability erosion dynamics

The notion of capability is widely invoked to explain differences in organizational performance, and research shows that strategically relevant capabilities can be both built and lost. However, while capability development is widely studied, capability erosion has not been integrated into our understanding of performance heterogeneity. To understand erosion, we study two software development organizations that experienced diverging capability trajectories despite similar organizational and technological settings. Building a simulation-based theory, we identify the adaptation trap, a mechanism through which managerial learning can lead to capability erosion: well-intentioned efforts by managers to search locally for the optimal workload balance lead them to systematically overload their organization and, thereby, cause capabilities to erode. The analysis of our model informs when capability erosion is likely and strategically relevant

Making the Numbers? “Short Termism” and the Puzzle of Only Occasional Disaster

Recent work suggests that an excessive focus on "managing the numbers"- delivering quarterly earnings at the expense of longer-term performance-makes it difficult for firms to make the investments necessary to build competitive advantage. "Short termism" has been blamed for everything from the decline of the U.S. Automobile industry to the low penetration of techniques such as total quality management and continuous improvement. Yet a significant body of research suggests that firms that sacrifice longterm investment to manage earnings are often rewarded for doing so. This paper presents a model to help reconcile the tension between these apparently contradictory perspectives. We show that if the source of long-term advantage is modeled as a stock of capability that accumulates over time, the intensity of the firm's effort to manage short-term earnings at the expense of long-term investment can have very different consequences depending on whether the firm's capability is close to a critical "tipping threshold." When the firm operates above this threshold, aggressively managing earnings smooths revenue and cash flow with few long-term consequences. Below it, managing earnings can tip the firm into a vicious cycle of accelerating decline. Our results have important implications for understanding managerial incentives and the internal processes that create sustained advantage. Keywords: capability; short-termism; system dynamics; tipping point; resource allocatio

Inter-phase feedbacks in construction projects

Understanding diverse performance trajectories of projects is of interest to operations researchers and practitioners. Interactions between multiple phases of a project are commonly assumed to be important in project dynamics, yet the strength of these feedback mechanisms has not been rigorously evaluated. In this study we use data from 15 construction projects to estimate the feedbacks between design and construction phases. The estimated factors reveal that undiscovered design rework diminishes construction quality and production rate significantly and construction completion speeds up the detection of undiscovered design rework. Together, these feedbacks can explain as much as 20% of variability in overall project costs. Comparison of model predictions with a separate set of 15 projects shows good predictive power for cost and schedule outcomes and their uncertainty. The estimation and prediction framework offers a template for using data from multiple cases to estimate both case-specific and industry-wide parameters of project models, and for leveraging those estimates for project planning

Understanding econo-political risks: impact of sanctions on an automotive supply chain

Purpose - The purpose of this paper is to introduce econo-political risks (EPRs) to supply chains (SCs). Based on case data from an automotive SC, this research identifies the mechanisms through which a subset of EPRs influences Sc operations and outcomes. Design/methodology/approach - An exploratory case study method is employed for theory development. Interviews with SC professionals of three case companies were the primary data source. Company documents, archival records, and direct observation provided further insights into how EPRs are perceived, how they impact a SC, how SC actors react to them, and what the overall performance results are. Findings - The research identifies EPRs in terms of scope (flow of material, money, and knowledge) and time, and provides concrete examples, along with the channels through which their impact unfolds, and the responses available to SC actors. The authors find secondary impacts of EPRs through economic and regulatory channels to be significant, and bankruptcy, strategic reorientation, and single sourcing are common outcomes. By elaborating on the mechanisms through which sanctions impact upon SCs, and the feasible response trajectories, this research can assist SC actors with more effective management of EPRs. Originality/value - This paper is novel for three reasons: first, it introduces EPRs to research into supply chain risk management (SCRM); second, it addresses SC risks in a developing country, a topic largely missing from the literature; and finally, this research focuses on post-event SC risks, whereas the bulk of SCRM literature focuses on the pre-event phase

Modeling the hypothalamus–pituitary–adrenal axis: A review and extension

Multiple models of the hypothalamus–pituitary–adrenal (HPA) axis have been developed to characterize the oscillations seen in the hormone concentrations and to examine HPA axis dysfunction. We reviewed the existing models, then replicated and compared five of them by finding their correspondence to a dataset consisting of ACTH and cortisol concentrations of 17 healthy individuals. We found that existing models use different feedback mechanisms, vary in the level of details and complexities, and offer inconsistent conclusions. None of the models fit the validation dataset well. Therefore, we re-calibrated the best performing model using partial calibration and extended the model by adding individual fixed effects and an exogenous circadian function. Our estimated parameters reduced the mean absolute percent error significantly and offer a validated reference model that can be used in diverse applications. Our analysis suggests that the circadian and ultradian cycles are not created endogenously by the HPA axis feedbacks, which is consistent with the recent literature on the circadian clock and HPA axis.United States. National Institutes of Health (R21MH100515

Blind Men and the Elephant: Detecting Evolving Groups In Social News

We propose an automated and unsupervised methodology for a novel summarization of group behavior based on content preference. We show that graph theoretical community evolution (based on similarity of user preference for content) is effective in indexing these dynamics. Combined with text analysis that targets automatically-identified representative content for each community, our method produces a novel multi-layered representation of evolving group behavior. We demonstrate this methodology in the context of political discourse on a social news site with data that spans more than four years and find coexisting political leanings over extended periods and a disruptive external event that lead to a significant reorganization of existing patterns. Finally, where there exists no ground truth, we propose a new evaluation approach by using entropy measures as evidence of coherence along the evolution path of these groups. This methodology is valuable to designers and managers of online forums in need of granular analytics of user activity, as well as to researchers in social and political sciences who wish to extend their inquiries to large-scale data available on the web.Comment: 10 pages, icwsm201