Louis Anthony “Tony” Cox: Drilling Into Politically Sensitive Risk Issues

Louis Anthony Cox, or “Tony” to members of SRA, is best known as Editor-in-Chief of the journal Risk Analysis since 2013, and as an Area Editor for mathematical modeling prior to that, from 2008 to 2013. Dr. Cox holds a PhD in Risk Analysis (1986) and an SM in Operations Research (1985), both from MIT; an AB from Harvard University (1978); and is a graduate of the Stanford Executive Program (1993). A long-time resident of Denver, Colorado, Tony has taught in the departments of mathematics, computer science, bioinformatics, and business analytics at the University of Colorado over the past 40 years. In 2020, he helped modernize the curriculum in Business Analytics to include new courses on Causal Analytics, Decision Analysis, Evaluative Analytics, Game Theory, and Artificial Intelligence for Business. Tony Cox is President of Cox Associates, a Denver-based company focusing on public and occupational health, safety, and environmental risk analysis; epidemiology; policy analytics; data science; and operations research. Since 1986, Cox Associates’ analysts and scientists have applied epidemiological, risk analysis, and operations research models to measurably improve health and environmental risk assessment and decision-making for public and private sector clients. In 2006, Cox Associates was inducted into the Edelman Academy of the Institute for Operations Research and Management Science (INFORMS), recognizing outstanding real-world achievements in the practice of operations research and the management sciences. In 2012, Dr. Cox was elected to the National Academy of Engineering (NAE), “for applications of operations research and risk analysis to significant national problems.” Along with his EIC role with Risk Analysis, Dr. Cox is Area Editor for real-world applications for the Journal of Heuristics. He has a long list of awards for research accomplishments, including multiple best paper awards, and he has over a dozen US patents on applications of artificial intelligence, signal processing, statistics, and operations research. We have deliberately focused this profile on several of his efforts to apply risk and decision science thinking and tools to politically sensitive issues. Cox's efforts to better understand these challenges have been welcomed by some and criticized by others. Our role is to try to present his views on these cases and thereby illustrate why risk analysis is such an interesting and fruitful endeavor. I have been fascinated by the possibilities and limitations of rational decision-making ever since. When I started my undergraduate work, I wanted to be a mathematical economist. I had taken an inspiring introductory economics course in high school and had come away with the impression that science and technology were more advanced than our understanding of how to use them wisely together to promote human flourishing. I hoped to apply mathematically rigorous thinking about decision-making and optimization to practical problems of cooperation, collective choice, productivity, and growth. I became interested in building causal models that would clarify the probabilities of consequences for different decision options and help to make clear and explicit where rational beliefs about probabilities came from based on available – usually incomplete – understanding of relevant causal mechanisms and processes. It seemed to me that decision analysis and game theory models usually just assumed some relationship between choices and outcome probabilities without justifying the assumed causal models. I wanted to understand how those models could be justified, so that even people with different subjective preferences for outcomes could agree on the objective factual aspects of a decision situation by agreeing on the outcome probabilities implied by different choices. I thought that decision analysis and game theory were seriously incomplete. Looking back from my perspective now, I would say that my college self was thirsting for a discipline of risk analysis that would help to determine the objective causal relationships among events, choices, and consequences because that seemed like an important hole in the theories of rational decision-making that I was studying. This course was a revelation to me. It complimented the much simpler perspectives on decision optimization that I was used to in decision analysis and game theory and got me thinking about the time consistency of beliefs, goals, and policies. None of the possible alternative non-causal explanations was consistent with the data, so by the process of elimination, we concluded that the suppression effect was a real causal effect. Charles made me co-author of his book, Beyond Probation: Juvenile Corrections and the Chronic Delinquent which was featured on The Today Show in 1979. I was 22, and this was my first book. It was a real thrill, of course, but it also introduced me to the challenges of causal analysis of data, as opposed to simply creating mathematical causal models. I've been fascinated by causal inference and modeling with observational data ever since. I consider such causal analysis to be fundamental for a lot of applied health risk assessment and epidemiology. I worked on an EPRI [Electric Power Research Institute] project quantifying the public and occupational health and safety risks from the same amount of energy produced using different fuel cycles. That led to my first solo publication, in the new journal Risk Analysis: “Artifactual Uncertainty in Risk Analysis” in 1982 (Cox 1982). That work won an award from ADL and gave me a chance to make a presentation to Chauncey Starr, then the president of EPRI. He really liked the work and encouraged my interest in risk analysis. I was hooked by this new field. One of the EPRI project managers, Paolo Ricci, became a lifelong friend and frequent collaborator in the early days of risk analysis. My boss at ADL, Joseph Fiksel, helped cofound the New England Chapter of the new Society for Risk Analysis, of which I became Secretary, and encouraged me to learn all I could about risk analysis and to work on all sorts of risk analysis projects at ADL. My Master's thesis on “Attribution of Risk in the Presence of Joint Causes” applied game-theoretic axioms and theorems to the problem of defining the fraction of blame for a jointly caused adverse outcome that should be attributed to each of the factors that increased its probability. This thesis, supervised by my kind, deeply insightful mentor and advisor Al Drake, who was the co-director of the Operations Research Center at MIT at the time, led to some papers in the journals Management Science and Risk Analysis that ended up being quite influential in the part of mathematical, epidemiology that deals with joint causation (Cox 1984, 1985). My 1984 paper in Risk Analysis on “Probability of Causation and the Attributable Proportion Risk” is still one of my favorites. Then in each set of activities that could lead to success, one should first try the available activity with the highest failure probability per unit cost so as to fail as early as possible if the set of activities could not all be completed. On the other hand, if there were also multiple different ways, or different sets of activities, whose completion would achieve success, then the set of activities that collectively had the highest probability of success per unit cost should be tried first. This basic idea had many applications – for example, to optimal sequential testing and repair in reliability systems (Cox and Qiu 1994; Cox, Chiu, et al. 1996) and to pursuit of goals in AI systems with uncertainty about which tasks can be completed, or with the ability to collect information or get questions answered before deciding what to do (Cox 1990a). Another application was figuring out how an intelligent attacker might try to compromise a target asset – either a physical asset or an information asset – through an attack consisting of a sequence of activities with different costs and success probabilities. Tony led an artificial intelligence project for ADL with the Air Force Computer Security Program Office, developing an “Automated Threat Assessment Methodology” (ATAM) that applied the same basic ideas as the R&D planning project to calculate the optimal attack strategies of intelligent attackers and to optimize defenses against them (Cox 1990b). I was annoyed by the widespread use of simplistic risk scoring methods and risk matrices that were clearly inconsistent with how a rational person or AI should or would do things That led me to write papers trying to clarify exactly what was wrong with such approaches and what ideas we could adopt from decision analysis, AI, and operations research to improve the modeling of optimal intelligent attacks and defenses against them. One of my favorites is “What's wrong with risk matrices?” which was published in Risk Analysis in 2008 (Cox 2008). This type of thinking became influential with a NAS committee I was on that focused on Methodological Improvements to the Department of Homeland Security's Biological Agent Risk Analysis (National Academies of Sciences, Engineering, and Medicine 2007) (See https://nap.nationalacademies.org/read/11836/chapter/2#2). This work helped to ignite a lot of game-theoretic and multi-level modeling by Vicki Bier, Seth Guikema, Jun Zhuang, and their students and many others that I think is now widely considered foundational for adversarial risk assessment. I have known Tony Cox for more than 35 years, since we studied with the same advisor in graduate school. Tony is perhaps the closest to a true polymath I have had the pleasure of knowing. I consider myself widely read, but am constantly astounded at the contributions Tony is able to make in integrating and applying the latest research results from a variety of fields, ranging from technical methods of correcting for multiple comparisons in statistics, to synthesizing the vast and complex literature on behavioral game theory, to what is known about the effects of diesel particulates on human health. Many practitioners (and even researchers) tend to stick to tried and true techniques, continuing to return to basic models and methods even when later developments in the field might be more appropriate for the problem at hand. By contrast, Tony is not only a voracious reader, keeping track of the latest developments in fields ranging from behavioral game theory to theories of carcinogenesis; he is also insightful in understanding where new methods or results could be useful in applications, and fearless in piloting such methods even in fields where they have not yet been applied. I have regularly coauthored with Tony (e.g., a book chapter on probabilistic risk analysis in my 2017 book on Risk in Extreme Environments), largely because I know he will inspire me to address interesting innovations and applications in a wider variety of fields than I might otherwise consider. The word “causality” is commonly applied to several distinct concepts, including attributing effects to factors with which they are associated; identifying the mechanisms by which changes in exposures change response probabilities; and predicting how outcome probabilities would change if we changed what we are doing. It is that third concept, sometimes called interventional causality (or, by some philosophers, manipulative causation) that I think is most relevant for most decision analysis and risk management applications. To me, data that provides no evidence that changing what we do changes outcome probabilities provides no evidence of a detectable interventional causal relationship between them. For example, if reducing high ambient levels of particulate air pollution in Ireland by more than 50% is observed to have no detectable effect on mortality rates over the following decades compared to areas that did not have that reduction, then these data provide no rational basis for a belief or prediction that reducing air pollution will create significant reductions in mortality rates (Cox 2024). The associations between air pollution and mortality may still satisfy other criteria for various types of causality, such as Bradford-Hill considerations for associative or attributive causation, but in my opinion, what matters for figuring out how to take causally effective actions to improve public health is just interventional causation. On the positive side, if statistical tests reject the null hypothesis of conditional independence between outcomes and actions, then there is reason to hope that changing outcomes can indeed change outcome probabilities. Technical methods such as Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots can then help to quantify the dependence of outcome probabilities and expected values on controllable inputs if all other inputs are held fixed (Cox 2023b). This opens the door to quantitatively informed decisions about what to do to make preferred outcomes, such as longer and healthier lives, more likely. This seems to me to be a much more promising approach to predicting and estimating effects of interventions than “causal” approaches based on subjective judgements about statistical associations. Developing mathematical models that could reproduce the main quantitative features of observed exposure-response data, such as their relative insensitivity to exposure concentrations above a certain level, and absence of an apparent increase in risk a sufficiently low concentrations, required considering molecular biological mechanisms in enough detail to identify major feedback loops and identify their mathematical stability properties (Cox 2011a). This could explain apparent epidemiological and experimental puzzles about why repeated high concentrations could be disproportionately dangerous, while total duration of exposure often mattered less. The key insight was to think of risk (e.g., transformed stem cells) as accumulating at a relatively high rate once chronic inflammation sets in, but at a much lower rate before that. The timing of the transition between these two regimes then drives lifetime risks, with the other details of exposure histories being much less important. I was thrilled when the NLRP3 inflammasome was and the molecular biological details of its and in the of chronic inflammation were as these causal biological mechanisms seemed to me to what the mathematical modeling required of the and implied about the of exposure-response relationships (Cox This understanding of and response is consistent with experimental exposure-response data in to such as or I had the to some exposure-response for these in the and it was clear that exposure was an basis for predicting biological since time of exposure such a role that exposures could create lower risks than exposures if they were more out over time et al. 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