Question: The title of your book is striking: The Unaffordable Price of Static Decision-Making Models. Why such a strong statement?

Co-editors: Because the stakes really are enormous. Static models are inherently short-sighted: by ignoring the trade-off between today and tomorrow, they overlook not only how today’s choices shape tomorrow’s opportunities and risks, but also how the future should influence the decisions we make today. In environmental and public health contexts, for example, the widespread use of cheap phosphate fertilizers in Europe led to cadmium accumulation in soils—a decision that static economic models did not flag as risky at the time. Today, this has become a major public health issue. These models are simple and elegant, but their very simplicity can make them blind to critical dynamics. Ignoring time may serve academic convenience, but it is costly for society and the environment. This is why we joined forces with leading scholars in dynamic modeling and decision sciences: together, the book demonstrates both the risks of static reasoning and the promise of dynamic approaches.

Question: But some would argue that static models are useful.

Co-editors: That’s true. Static models have their place. They’re great for clarifying mechanisms, providing intuitive understanding, and performing “thought experiments.” Reduced-form or structural static models can help when a phenomenon is still poorly understood. The problem arises when these static simplifications become the default methodological approach. Then you risk ignoring stock effects, dynamic interactions, and long-term trade-offs. In practice, that can lead to seriously misleading conclusions and policies. Several contributions in the book illustrate this clearly, showing how static shortcuts can hide crucial long-term dynamics.

Question: The subtitle mentions Challenges in Economics and Management Science. What challenges are you referring to?

Co-editors: The main challenge is conducting research that is both rigorous and relevant. Static simplifications remain pervasive in research, teaching, and even policy advice. Yet most real-world problems are dynamic, with evolving environments, multiple actors, and long-term consequences. Ignoring this temporal dimension produces results that are incomplete or misleading. Our book is a call to researchers, editors, funding agencies, and policymakers: oversimplifying reality comes at a high cost. Environmental issues, technological change, and strategic interactions all illustrate how static models can fail.

Question: You argue that farsighted approaches enable more responsible research. Could you expand on that?

Co-editors: Responsible research, in this sense, is about preserving livable conditions for future generations, it’s about ensuring that the consequences of today’s choices are visible and considered. Dynamic approaches force us to think about sequences of decisions and the accumulation of resources or damages over time. They make the evolution of uncertainty explicit. In environmental economics, for example, they reveal how emissions today affect future generations and the sustainability of ecosystems. For instance, dynamic models of climate policy show that introducing cleaner technologies without global coordination can backfire, increasing total emissions, the so-called rebound effect. Similarly, agroecological models reveal that short-term gains from expanding agricultural land may destroy biodiversity over time if the long-term feedback between ecosystems and productivity is ignored.  This long-term perspective is what makes research responsible: it accounts for consequences over time rather than focusing only on immediate outcomes. The contributors to this book provide many such examples, across disciplines, that underline why dynamic thinking is indispensable.

Question: How do dynamic approaches influence research practices more broadly?

Co-editors: They foster interdisciplinarity and system-wide thinking. Many economic and management problems intersect with ecology, public health, and technology. Such interdisciplinary integration almost inevitably requires the use of state-space models, which provide a unified representation of systems evolving over time. State-space formulations make it possible to link economic, environmental, and technological subsystems within a common dynamic framework, allowing feedbacks and cross-domain effects to be analyzed consistently. For instance, integrating epidemiological and economic dynamics during a pandemic, or connecting energy consumption, emissions, and technological change in climate policy analysis, both rely naturally on state-space modeling. Dynamic frameworks allow researchers to integrate these dimensions, helping to understand the bigger picture. For example, age-structured dynamic economic models can show how pollution affects mortality, fertility, and utility over generations, revealing trade-offs that static models cannot capture. In supply chain management, dynamic models uncover how short-term inventory or production decisions ripple through the system over time, highlighting vulnerabilities that static approaches would miss. By capturing these intertemporal and cross-sector interactions, dynamic approaches improve the rigor and relevance of research, encouraging policies that are robust and socially responsible.

Question: How is the shift toward dynamic approaches possible in practice?

Co-editors: The shift is feasible but requires coordinated changes across the research ecosystem. Practically, researchers need to embrace dynamic methods in their own work, not just as a technical exercise but as a way to capture long-term consequences and intertemporal trade-offs. Journals and reviewers should recognize the value of these approaches, ensuring that editorial boards include experts in dynamic modeling and that submissions are fairly evaluated. Funding agencies can provide incentives for projects that explicitly incorporate intertemporal analysis.

Academic institutions also have a critical role: they could integrate dynamic methods into PhD programs, recruit faculty skilled in these approaches, and support research that embraces intertemporal and system-wide perspectives. Currently, however, static models—particularly linear and nonlinear programming—still dominate the operations research and management science curriculum. These methods are undoubtedly valuable, but their predominance leaves limited room for dynamic approaches such as optimal control, dynamic programming, or differential game theory. Rebalancing the curriculum is essential if we want future researchers to think intertemporally rather than statically. It’s a cultural shift: valuing analytical depth and realism over simplicity for its own sake. Students trained in these methods are better equipped to tackle complex, long-term problems in economics, management, and policy. Dynamic frameworks naturally encourage system-wide thinking, allowing researchers to link economics with environmental science, public health, energy systems, and technology. Over time, these coordinated changes can gradually shift both the culture and the methodology of economics and management research, promoting a more farsighted, responsible, and impactful science.

Question: Some might still worry that dynamic models are too complicated.

Co-editors: Complexity is real, but it’s not an excuse to ignore it. Even if a model cannot capture every detail, it highlights critical patterns, feedback loops, and long-term consequences. Static models may seem simpler, but they can completely miss essential dynamics, producing misleading guidance. The goal isn’t to make modeling unnecessarily difficult; it’s to avoid oversimplification that compromises insight and responsibility.

Question: Any final message for researchers and students?

Co-editors: Farsighted thinking is essential. Considering future consequences, uncertainty, and interdependent systems makes research both academically rigorous and socially meaningful. Our book is a warning: simplifying reality for convenience may be tempting, but it comes at our own expense. After decades of reflection on modeling in economics and management, and enriched by the collective work of leading scholars in dynamic decision-making, our conclusion is clear: the problems around us—environmental, technological, social—constantly remind us that responsible research must account for dynamics.