Place as Method, Theory, and Responsibility: Rethinking Scientific Inquiry for Complex Social Issues

Place is not a variable: Complexity, systems thinking, and the limits of simple explanations

This session is not an argument against simple models, which can be useful, elegant, and necessary. However, simple models become dangerous when they train us to think simplistically. Further, complex models do not guarantee the requisite complex thinking about place. Here, scholars will share work that couples complex thinking with both ordinary tools (e.g., interviews, regression models, maps, archives, surveys, administrative data, or descriptive comparisons) and with big data or systems models.

Whose theory explains this place?

This session begins from the premise that theory matters — but not all theory matters in the same way, and not all theory is strong theory. Sometimes theory clarifies concepts, mechanisms, and evidence. Sometimes it simply reproduces a disciplinary inheritance. Sometimes it helps us see power, history, land, and relation. Our task today is to ask: What makes a theory or framework useful; Who gets to make theory about place; and How do we move from an idea about place to a researchable and revisable explanation?”

Causal thinking is not the same as causal inference

This session begins with a distinction that is easy to say but hard to practice: causal thinking is not the same thing as causal inference. Causal inference methods can be powerful, but they do not exhaust what it means to reason causally. Indeed, we might consider causal thinking through any of the empirical strategies we use; otherwise our work leaves us with correlations that imply more than they can support. Further, we are asking how we consider evidence from multiple sources that may seem contradictory even, as we build a causal story of the social world (evidentiary pluralism). We are asking what it means to think in causal ways about place: about conditions, mechanisms, histories, background structures, feedback loops, selection, and intervention. We are also asking what happens when the causal field is visible to some researchers and invisible to others. 

Understanding, not proving: Critical theory and rigorous scientific inquiry

Strong scientific inquiry matters; researchers should ask questions to understand our social world, not to confirm a belief; Good research requires logic, evidence, openness to being wrong, careful inference, conceptual clarity, and willingness to revise claims. Critical theory can make scientific inquiry stronger, as it can reveal hidden assumptions, broaden what counts as relevant evidence, identify omitted causal fields, challenge narrow concepts, and expose how power shapes research questions. The session should reject two unhelpful extremes: (a) scientism without critique, or the idea that conventional scientific methods are neutral, complete, and untouched by power; and (b) critique without inquiry, or the idea that -- because much of our science on place has been shaped by colonialism, racism, sexism, ableism, and hierarchy -- rigorous standards of evidence, logic, and inference are merely oppressive tools. Strong critical theory and strong scientific inquiry should discipline each other. Critical theory helps us ask better questions and see more of the world. Scientific inquiry helps us avoid mistaking conviction, moral urgency, or political commitment for evidence.

The Dataset Does Not Know the Place

Presentation by ISR Artist-in-Residence Rick Lowe, followed by a dialogue between Kate Cagney and Rick Lowe

This session explores cities as complex systems through several different forms of evidence and modeling: scaling models, mobility data, and participatory approaches. We are not asking whether any one method fully captures the city. Rather, we are asking what each method helps us see, what it simplifies, and how interdisciplinary collaboration can make these tools more meaningful for understanding cities as lived, governed, historical, infrastructural, and unequal places. 

Unlike the cities session, this session does not center a single formal systems modeling tradition. That is intentional. Rural and small-town places are often poorly served by importing urban systems models. Instead, this session examines rural complexity through regional economies, digital infrastructure, participatory systems modeling, environmental health, and political economy. The goal is to ask what kinds of data and models help us understand rural places relationally — and what we miss when rurality is treated as residual, deficient, or disconnected from broader systems. 

Unlike the cities session, this session does not center a single formal systems modeling tradition. That is intentional. Rural and small-town places are often poorly served by importing urban systems models. Instead, this session examines rural complexity through regional economies, digital infrastructure, participatory systems modeling, environmental health, and political economy. The goal is to ask what kinds of data and models help us understand rural places relationally — and what we miss when rurality is treated as residual, deficient, or disconnected from broader systems. 

The academy as a place: incentives, networks, and the conditions for better science

The academy is not just where research happens. It is a place that shapes what questions are valued, who gets trained, who gets time to think, who gets mentored, who gets cited, who gets protected, who gets funded, and what kinds of knowledge become legible as “excellent.” This session should be honest about the academic model: It rewards speed, quantity, novelty, individual ownership, network privilege, citation performance, disciplinary legibility. It punishes slow, collaborative, interdisciplinary, community-engaged, methodologically careful, or theoretically deep work. But the session should also be constructive: How can scholars build better intellectual homes? How can early career scholars navigate without simply assimilating? How can senior scholars redistribute access to mentorship, methods, theory, collaboration, and time? How can we evaluate scientific contributions beyond citation counts and paper volume? How can people decide what kind of academic or nonacademic career will let them do the work they value? The goal is not despair. The goal is clear-eyed agency. 

This session asks us to move beyond the residential-neighborhood default. Neighborhoods are not isolated containers, and people’s exposures do not stop at the boundaries of their home census tract. Neighborhoods are linked through housing markets, mobility, organizations, institutions, and spatial data systems. Today’s speakers will help us think about how models, mobility data, qualitative research, and critical spatial theory can work together to better understand dynamic neighborhood life.

The potential of interdisciplinary collaboration to build the science on place