At least three major themes are woven into my teaching, research, and college service: acknowledgment of the importance of practice, confidence in the fruitfulness of adaptive learning processes, and an abiding interest in how societies structure time.
Questions that came to mind as I was teaching econometrics spurred me on to investigate its history. I was struck by the fact that in economics we are often trying to apply statistical techniques originally designed for static comparisons to explain temporal changes. My curiosity led me to research and write a book on the history of time series analysis. I discovered that many of the data manipulation techniques we use to apply the statistical theory of “deviation from the mean” to empirical investigation of “change over time” were grounded in commercial practice.
In my classes, history is a useful part of the referencing of abstract to concrete. Students in my data analysis courses have found the theories and techniques of statistics more approachable as the historical dimensions of “why” and “when” dimensions are added to the practical dimensions of “how” and “what.” So in Social Science Statistics we start in the seventeenth century with how John Graunt’s use of the Merchant Rule of Three (a is to b, as c is to d) on deaths due to plague and other causes initiated the disciplines of statistics, demography, epidemiology, and actuarial science. In my Women and Economics course, a structuring of a history of agricultural technology in terms of population density sheds light on when women’s work was of a public nature and of relatively high status or when it was accompanied by seclusion. Similarly, in senior seminar I use an economic history of capitalism and colonialism, including literary references to factory discipline in the eighteenth century, to give context to the potency of Adam Smith’s assertion that “the division of labor is limited by the extent of the market.” That in turn enables economic students to grasp the notion of increasing returns as it is now applied to the information economy.
My latest research project, funded by both the National Humanities Center and the National Science Foundation, also has a strong focus on the importance of practice to the development of statistical and economic theory. During World War II and the Cold War, scientists and engineers working in government-funded multidisciplinary groups developed mathematical models and protocols for optimizing mechanisms of warfare. Economists, who are well trained in the mathematics of minimizing costs subject to constraint contributed to this, but also borrowed from the subsequent developments. So, for example, a mathematical model of how an effective gun sight in a bomber turret processed information eventually became a Nobel-price winning model of how consumers respond to the uncertainty of future income.
A key feature of the wartime servomechanisms I studied was the systematic incorporation of feedback -- the difference between the desired and actual states served as input into the process of controlling the actual condition. Similarly, my teaching style builds on adaptive feedback loops - between theory and practice and between actual and desired. I continually challenge my undergraduate students to put newly learned abstract concepts into practice, whether it be encouraging them to see the usefulness of economic notions of tradeoffs and opportunity costs to their personal decision making or asking them to analyze an article from the national press. Their daily confrontation of concrete descriptions with theory or analysis adds relevance and career preparation to their courses. It also helps them acquire new appetites for learning and expression. Students in my data analysis courses do several structured, independent research projects that hone their skills in marshalling facts, revealing patterns, and inferring relationships between variables. The adaptive learning process is also evident in the connections between my data analysis courses and my statistical analysis for institutional research for Mary Baldwin College. I apply what I teach, I learn from that application, and then I adapt my teaching.
I concur with Alfred North Whitehead’s response to the question of whether he considered facts or ideas more important. He answered “Ideas about facts.” I am particularly struck by ideas about patterns in data over time. I am cognizant of our human propensity to over interpret and indeed create unwarranted patterns through an order-biased perception. Within the context of that caution, I relish observing the seasonal variations in nature or diurnal variations in stock markets, playing around with a variety of graphs to comprehend changes in social phenomena and Mary Baldwin College, and constructing historical narratives to understand how scientific disciplines and human cultures developed. These pursuits are a main source of my awe, respect and at times despair for the world we live in. With enthusiasm for the subjects I teach, insights I have gained from my research, and daily demands for student input into their own learning process, I try to kindle a similar critical reflection in my students.