Preface

The simplest model of health is balance. However even simple balancing processes, when interacting, give rise to complexity. The balancing of resources between individuals in collectives, as well as between individual parts of metabolic systems, gives rise to complex public health systems.  Even a single person balances costs and potential benefits when making health decisions, but interacting individuals can share costs and benefits. This produces opportunities and dilemmas. Public health faces tensions between individual and collective, local and global, diversity and uniformity, disparity and equity, selfishness and cooperation. Such tensions make public health complex. The good news is that much complexity has simple underlying processes. We can find enough interesting puzzles to keep us studying for years, but also find explanatory models simple enough to be immediately practical.

My interest in modeling simple dynamics generating complex collective phenomena stem from a root desire to find practical solutions to public health problems. For many years my idea of a practical approach to public health problem solving was helping community organizations create “logic models” of what change they expect, a set of “IF actions A & B THEN we get output C” type of model.  Often the tendency in such logic modeling was to add all kinds of complications, such as resource disparity, seemingly beyond the capacity of any individual person or organization to do anything about. I often found myself torn between suggesting we simplify by focusing on the micro-level individual behavior we could address, as opposed to addressing the structural and collective level factors.  In this book, I offer an alternative approach. I offer models simple enough to play as a game with checkers and coins on a board, representing behaviors and resources, but leading to collective phenomena sophisticated enough to require computer modeling to appreciate all the outcomes.

A breakthrough for me occurred when I discovered a model that elegantly represented many of the tensions we see in public health, including individual versus collective interests, cooperation versus selfishness, and also local versus global perspective. The model is simple enough to demonstrate with checkers on a board. Fill all the spaces on a checkerboard with randomly distributed red and black checkers. Every black cooperates and every red defects. Every checker gets one unit of benefit for every cooperator (black) surrounding them. Every cooperating checker also pays one unit cost per neighbor, which is the cost of cooperating.  Then every checker “imitates” the local neighbor individual with the highest score. So that if a cooperator finds he has the highest score he stays a cooperator, but if he has a defecting neighbor who did better than himself or his seven other neighbors, then he defects—we replace that black checker with a red checker (from a bag with plenty of extra checkers). This selection of the locally best behavior is analogous to evolutionary selection. Repeating these types of rules over and over makes this game “evolutionary.”  One sees what behaviors evolve in the population of individuals inhabiting this checkerboard. We can see how many individuals cooperate and how many defect, and in what spatial patterns. Then repeating the game again and again for different values of benefit and cost and we see how the patterns of behaviors evolve in our board game. We see cooperation thrive in local clusters, communities, even if globally speaking there are more defectors than cooperators. We see different levels of total accumulated wealth (total benefits earned minus costs paid), local and global disparities (clustering of wealth). 

I found many ways to incorporate health into these types of evolutionary games, to model various health behaviors in the context of cooperation, resource distribution, and local community self-organization.  Repeating the game plays over and over reveals emergence of some very interesting collective behaviors, including cooperative health protective action and various types of bottom-up organization. I had found simple models that explained a great many complexities in public health behavior.  Social eating, exchange of goods, or any repeated act of taking in resources and paying costs can evolve into complex and unexpected results, when physiological or social feedback is involved. Taking this approach, I found or developed many evolutionary games relevant to various public health behavioral phenomena. Another game with red and black checkers shows the emergence of collective intolerance for diversity and the emergence of neighborhood segregation (clustering by color) despite individual tolerance.  Some models even apply to the microbial level behavior, for example, a simple rock-paper-scissors game, played with three colors of chips on a board is a good model of the emergence of tolerance for diversity among bacteria.

Seeing parallels and connections between microbial and human behavior was particularly satisfying for me as a way to reconnect to my earliest training.  I had started out aspiring to address public health disparities at the micro-biological level, hoping to find cures for infectious diseases afflicting less fortunate countries.  In those days the following sentence was getting much attention: “Nothing in biology makes sense except in light of evolution” (Dobzhansky 1973, reprinted 1983). But evolutionary processes seemed to me irrelevant to immediate and concrete problems of disease. I was still very far from making the connection to human behavioral ecology. I was lost in the micro-level details of the laboratory. Until, my lab partner challenged me, questioning the relevance of biology to the health of developing countries, “Isn’t that more a political and economic issue?” Hearing that amidst the test tubes and Bunsen burners made the idea more appealing. I turned to community public health.  Yet there was no escaping these issues of individual part versus collective whole, whether in the biology lab or in the field of public health. A parallel tension is between focusing on a local level, a specific community, or a larger federal or state level.  In “international public health,” the debate is between intervening at the level of an individual nation or region or at the global level. These debates and issues also permeate economics, sociology and politics, expressed most generally as a tension between individualism versus collectivism, self-interest competing with collective welfare.  That dilemma is a theme of many evolutionary game models.  In fact, many features of evolutionary games are consistent with a population perspective on health and behavior. In evolutionary games, each individual’s “fitness” depends upon their behavior and also upon the behaviors of others, and behaviors depend upon intentions, expected benefits and costs, and other beliefs in the collective.  Ultimately, whether or not behaviors are healthy, sustainable and equitable depends upon the actions of the entire population.  Even simple rules of action lead to complexity in evolving interaction populations. These evolutionary games, in turn, give rise to “complex systems.” Evolution leads to complexity, to “endless forms most beautiful” (Darwin 1859). Thus the main premise guiding the modeling approach I take in this book is:

Public health behavior makes the most sense in light of evolutionary games.

Even one individual’s health behavior is like two or more persons playing a cooperative game. We have intentions, but satisfying these intentions depends upon other individuals. When considering initiating a health behavior, the present self considers whether or not the future self will follow-through. To execute the long-term behaviors necessary to achieve health, we need to trust our own bodies to be able to persevere, much as we often need to trust others to be trustworthy. In health behavior, having good intentions is not enough. Moreover after each incremental behavior, individuals can learn, adjusting beliefs, intentions and behavioral strategy, based on the performance of one’s past self.  Our conscious mind also plays coordination games with our physiology. One’s physiology often has a different strategy than one’s mind.  We need models of interactive balancing, even to understand a single person’s behavior. The conscious mind wants to lose weight, for instance, while one’s physiology tries to maintain weight, balancing loss of fat with a slower metabolism and increased anticipated rewards from eating.  The reward-circuitry in the brain has its own way of balancing perceived future benefits with current rewards, temptations and threats. Essentially we play repeated cooperation games with our selves. Thus the evolutionary game models apply quite well to public health behavior at all levels.

Christopher Keane, January 2013