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1. Moneyball: The Computational Turn in Professional Sports Management

   Hintz, Eric S. (April 2022, Papers of the Business History Conference)

   In this recent history, I describe how the embrace of computational analytics has transformed the management of professional sports in the 21st century. Sports analytics encompasses a set of data management technologies and mathematical techniques for interpreting observable statistical data about athletes and game play to help general managers, coaches, and players make better decisions and attain a competitive advantage. General managers use analytical information to evaluate players for drafting, trades, and contract-salary negotiations. Coaches and players use analytics to understand competitors’ tendencies, develop in-game strategies, and identify areas for training and improvement. Essentially, analytics is the application of “scientific management” (Taylor, 1911) to sports. Accordingly, the paper situates the twenty-first century Moneyball phenomenon (Lewis, 2004) in the context of a much longer history. Drawing on published primary sources and contemporary news coverage, I trace the evolution and gradual professionalization of the sports analytics community, which emerged from an eclectic group of postwar operations researchers, hobbyists, and fringe freelance journalists. I argue that the computational turn in professional sports has created competitive advantages for certain teams and directly influenced players’ in-game strategies. Moreover, this analytical turn has initiated a shift in epistemological authority in the front office. As professional teams have learned to “trust in numbers” (Porter, 1996), they have increasingly rejected the traditional expertise of former players and scouts and let the statisticians and “computer boys” take over (Ensmenger, 2012), albeit with predictable resistance. Advocates suggest that analytics have made the games fairer and leveled the playing field for teams with smaller payrolls. Meanwhile, critics suggest that analytics have turned players into automatons and robbed the games of individual creativity and spontaneity. Dear program committee: This individual paper could fit well in a panel on applied management, sports, computing, innovation, or STS. 

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2. Reframing wildlife disease management problems with decision analysis

   https://doi.org/10.1111/cobi.14284  

   McEachran, Margaret_C; Harvey, Johanna_A; Mummah, Riley_O; Bletz, Molly_C; Teitelbaum, Claire_S; Rosenblatt, Elias; Rudolph, F_Javiera; Arce, Fernando; Yin, Shenglai; Prosser, Diann_J; et al (May 2024, Conservation Biology)

   Abstract Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade‐offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision‐making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision‐making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision‐making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white‐nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision‐making, including competing objectives, risks, prediction uncertainty, and limited resources. 

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3. Soil as an Archetype of Complexity: A Systems Approach to Improve Insights, Learning, and Management of Coupled Biogeochemical Processes and Environmental Externalities.

   https://doi.org/https://doi.org/10.3390/soilsystems5030039  

   Turner, B.L. (July 2021, Soil systems)

   Antonio Martínez Cortizas (Ed.)

   Due to tightly coupled physical, chemical, and biological processes that often behave in nonlinear, counterintuitive ways, it is argued that soil is an archetype of a complex system. Unfortunately, human intuition and decision making has been shown to be inadequate when dealing with complex systems. This poses significant challenges for managers or policy makers responding to environmental externalities where soil dynamics play a central role (e.g., biogeochemical cycles) and where full ranges of outcomes result from numerous feedback processes not easily captured by reductionist approaches. In order to improve interpretation of these soil feedbacks, a dynamic systems framework is outlined (capturing feedback often excluded from investigation or left to intuition) and then applied to agroecosystem management problems related to irrigation or tillage practices that drive nutrient cycling (e.g., soil water, nitrogen, carbon, and sodium). Key soil feedbacks are captured via a variety of previously developed models simulating soil processes and their interactions. Results indicated that soil system trade-offs arising from conservation adoption (drip irrigation or no-tillage) provided reasonable supporting evidence (via compensating feedbacks) to managers justifying slow adoption of conservation practices. Modeling soils on the foundation provided in the complex systems sciences remains an area for innovations useful for improving soil system management 

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4. The Complexities of Counting Fish: Engaging Citizen Scientists in Fish Monitoring

   Bieluch, Karen H; Willis, Theodore; Smith, Jason; Wilson, Karen A. (January 2017, Maine policy review)

   Data gathered by citizen scientists can help ecologists understand long-term trends and can improve the quality and quantity of data about a resource. In Maine and Massachusetts, numerous citizen science programs collect data on river herring, anadromous fish that migrate each spring from the ocean to spawn in rivers and lakes. In collaboration with state and local resource managers and academic institutions, these programs aim to protect and restore river herring, improve local watersheds, and in some cases, support commercial harvesting. To better understand how programs are run and how data are used by managers, we interviewed program coordinators and resource managers. Interviews revealed that resource managers consider citizen science–generated river herring data in decision making, but that their concerns about data quality affect if and how data are used. Although not without challenges, standardizing monitoring approaches could improve data collection and use. We offer six considerations related to standardization for managers. 

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5. Factors influencing wildfire management decisions after the 2009 US federal policy update

   https://doi.org/10.1071/WF23129  

   Fillmore, Stephen D.; McCaffrey, Sarah; Bean, Rachel; Evans, Alexander M.; Iniguez, Jose; Thode, Andrea; Smith, Alistair M.; Thompson, Matthew P. (January 2024, International Journal of Wildland Fire)

   Background The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors. Aims To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009. Methods Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed. Key results Most previously known decision factors as well as numerous emergent factors were identified. Conclusions To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers. Implications Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes. 

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