Solving Wicked Problems
Updated: Jun 11, 2019
Do you ever wonder why scientists can’t seem to agree? Why do nutritionists reach different conclusions regarding the risks and benefits of eating fats, carbohydrates, red meats, eggs, and sugar? Why did it take scientists so long to reach a consensus concerning the causes of climate change or the health risks of tobacco smoke? Why do competent economists disagree about causes of and solutions to just about any economic problem that arises?
Scientists disagree because these are all what social scientists refer to as “wicked problems.” A wicked problem is difficult or impossible to solve because of the inability to collect and analyze enough information to draw irrefutable conclusions. This inability is due to the complexity, interconnectivity, and dynamic nature of the systems within which such problems arise. In such cases, it is virtually impossible to isolate specific causes and effects. Different scientists draw different conclusions from different subsets of information and from studies at different times. Apparent causes actually may be the effects of other causes somewhere in the system. Effort to solve one aspect of wicked problems may reveal or create other problems. The controversies surrounding conflicting conclusions are magnified by the number of people affected and the economic consequences of potential solutions.
For example, in nutritional studies it is virtually impossible to isolate the effects of eating a specific food or food group from all other factors that might affect a specific aspect of human health. Nutritionist often attempt to adjust for obvious factors such as age, gender, ethnicity, physical activity, and such. However, there are a multitude of physical, mental, and environmental factors that can affect the health of individuals in a wide variety of ways. Conflicting conclusions are controversial because everyone makes food choices, and those in the food industry have powerful economic interests to defend.
With respect to environmental problems, such as global climate change, the first principle of ecology is that “everything is interconnected” – you can’t do just one thing. So by definition, ecological causes and effects cannot be isolated. It is impossible to isolate the human contribution to greenhouse gasses from contributions of soils, oceans, on other animals because human activity affects everything else on earth and everything else affects human activity. Again, addressing the problems of global climate change will affect just about everything, and the economic stakes are enormous.
The data necessary for social and economic studies inevitably reflect the choices of people. People are not machines. They do not all make the same choices and don’t necessarily repeat the same choice and actions over time. People are continually trying to solve old problems and exploit new opportunities. As they do, their choices, actions, and reactions change. Scientists are also people. It’s impossible for scientists to isolate their particular worldviews and belief systems from their scientific observations and conclusions, particularly when those observations involve other people and their conclusions affect their professional success.
Scientists often rely on statistics to cope with these complexities. However, the validity of statistical results depends of certain underlying assumptions. For example, deviations from specific cause-effect relationships, called statistical errors, are assumed to be purely “random” rather than associated with other causal factors. All of the relevant information is assumed to be included in the analysis, leaving no significant probability of unobserved causes. Also, the conclusions regarding cause and effect relationships are relevant only for the conditions that existed at the time of the study. The results may or may not be useful in solving problems under real-world conditions at a later point in time. Statistical analyses are simply not capable of coping with the incomplete information and the internal complexities and dependencies that characterize wicked problems. In such cases, statistical conclusions at best provide rough approximations, and at worst, are outright misleading.
As a result, it often takes a long time for scientists to reach a consensus concerning solutions to “wicked problems.” A consensus emerges only when enough different scientist have reached similar conclusions in addressing a particular problem to create confidence in a common general conclusion. The conclusions of the individual studies need not be identical to support general conclusions. However, the number of studies drawing similar conclusions must increase over time in relation to the number reaching contradictory conclusions. A consensus grows stronger over time if it is confirmed by changing real-world conditions. This is the basic process by which a consensus was formed linking tobacco smoking to human health and by which the consensus is strengthening that human activity is a major cause of climate change.
I think consensuses also are emerging in a number other important areas that can be characterized as wicked problems. In such cases, I have come rely more on the meta-studies, rather than referencing specific individual studies. The scientists who conduct meta-studies attempt to review all of the available research relevant to the particular subject or problem and draw general conclusions. The individual studies in a meta-study typically have been conducted by scientists with a variety of different perspectives and interests over an extended period of time.
By their very nature, meta-studies identify cause and effect relationships for systems or problems in general. For example, meta-studies that relate to concentrated animal feeding operations or CAFOs include studies of different CAFOs in different communities, but the studies all focus on effects of the same basic system of animal production. The general cause and effect relationships found in a meta-study may or may not be apparent for particular situations or individuals. The underlying cause and effect relationships for CAFOs may be magnified or obscured by the complexity of interrelationships for particular CAFOs in particular communities but are revealed for the system as a whole in meta-studies of CAFOs.
Examples of meta-studies relevant to the wicked problems that I tend to address include:
Negative Ecological, Social, and Economic Impacts of CAFOs:
Pew Commission Says Industrial Scale Farm Animal Production Poses “Unacceptable” Risks to Public Health, Environment
Impact of Industrial Farm Animal Production on Rural Communities
Antibiotic Resistant Bacteria in CAFOs Linked to Human Health:
Federal Agencies Need to Better Focus Efforts to Address Risk to Humans from Antibiotic Use in Animals
Antibiotic Resistance Threats in the United States, 2013 Executive Summary
Negative Impacts of Industrial Agriculture:
Industrialized Farming and Its Relationship to Community Well-Being
From Uniformity to Diversity; A paradigm shift from industrial agriculture to diversified agroecological systems
Nutritional benefits of organic/sustainable foods:
Social and Ethical Essentials of Human Happiness: