Lynne Kiesling
UPDATE: I’ve had a report that the link to the slides is not working, but I can’t get it not to work … so if you are having trouble please let me know in the comments and I’ll go bug-stomping. UPDATED UPDATE: mischief managed, I think!
On Saturday I was honored to be the morning keynote speaker at the Students for Liberty Chicago Regional Conference. In only four years SFL has grown into a large and effective organization for bringing together students who share an interest in exploring and promoting individual liberty, classical liberal ideas, and public policy that reflects those principles.
My talk, Beneficial Complexity (pdf of slides), took some basic economic concepts and looked at them through the lens of complexity science. My main objective was to encourage the attendees to see ways to integrate some core classical liberal ideas into their own thinking, their own work, their persuasive discussions with others, and their advocacy activities.
Start with a story: a flower market selling calla lilies grown in Colombia. Lots of buyers and sellers, lots of parties involved in getting the flowers from places like Colombia to your neighborhood flower shop. Mostly impersonal exchange, but not entirely devoid of personal relationships. And without any one person knowing how to do so in its entirety, the flowers get from Colombia to my flower shop for me to buy. If you are familiar with Leonard Reed’s I, Pencil essay about the highly decentralized yet coordinated processes that combine to bring pencils to consumers, you recognize this theme. One of the fundamental economic and epistemological concepts that I, Pencil illustrates is the knowledge problem — no one person knows how to make a pencil, or how to grow and sell calla lilies globally. We associate this idea primarily with the work of F.A. Hayek, as stated in his famous essay “The Use of Knowledge in Society” in 1945 (which also inspires the title of this blog).
Knowledge is dispersed and, importantly, it is also private and often subjective. Your willingness to use some of your resources to buy a cup of coffee, or a pencil or a calla lily bouquet, is known only to you and is context dependent, which means that much of the knowledge that goes into individual decisions is local and hard to centralize. Yet calla lilies show up in Chicago shops, as do pencils, in ways that satisfy the preferences of consumers and profit producers along the supply chain. How does that happen? Markets and prices create networks of dispersed, local, private knowledge that connects and aggregates that knowledge and sends valuable signals to economic actors about the relative benefits and costs of the decisions they take.
When we think about economic activity taking place in networks, and how exchange and prices connect networks and extend and deepen them, we are using the tools of complexity science to understand economic behavior. Think back to the flower market and the pencil. As Eric Beinhocker writes in The Origin of Wealth, “The complexity of all this activity is mind-boggling. … all the jobs that must get done, all the things that need to get coordinated, and the timing and sequence of everything. … there is no one in charge of the global to-do list. … Yet, extraordinarily, these sorts of things happen every day in a bottom-up, self-organized way. … The economy is a marvel of complexity.” (2006, pp. 5-6)
Technically speaking, what is a complex system? It’s a system or arrangement of many component parts, and those parts interact. These interactions generate outcomes that you could not necessarily have predicted in advance. In other words, it’s a non-deterministic system. Scholars in many different fields use this general idea to study a range of systems and phenomena, from molecular and cellular interactions in physics, chemistry, or biology, to the organization of the brain in neuroscience, to species and environment interactions in ecosystems, to cascading network failures in electric power systems, to networks of co-author collaborations in particular fields of research, and many other applications.
These applications share an interest in three features of a complex system: its structure (how are the parts connected, how do they interact?), its rules (physical or human-generated), and its behavior. apply this idea of a complex system to our economic and social interactions. Go back to Beinhocker’s description of the market economy as “a marvel of complexity” in which all sorts of activities get coordinated in a “bottom-up, self-organized way”. Think of the economy as a complex system, in which individuals are the agents, the component parts, with dispersed private knowledge. We are connected in many ways — social relations, economic exchange, organizations, and so on — and our interactions shape individual decisions, individual behavior, and ultimately overall system behavior. The profound insights of writers like Ferguson, Smith, Hayek, and others is that individual agents have their own preferences and private knowledge, but we interact, and in so doing we generate system-level behavior that is generally self-organized — emergent order. In this unplanned order no one person can predict the specific outcome we’ll achieve, but we still experience over and over and over in human history that order generally does emerge.
But order doesn’t necessarily always emerge, and the order that emerges sometimes isn’t pretty. That observation leads to the elephant in the room with respect to emergent order in social system: the rules. All exchange takes place within a framework of rules, an institutional context. Those institutions include formal laws enforcing property ownership, contracts, and punishment for theft and fraud, as well as informal social norms and peer pressure that may, for example, affect how the bargaining and negotiation in the exchange take place. Rules shape how agents interact, and shape their incentives … and as a result they can also affect system behavior. One important implication of studying economies as complex systems is that when we design institutions, we should model and test and strive for rules that enable order to emerge, which means an emphasis on process rather than using rules to achieve some specific outcome. That’s one important intersection of classical liberal principles with complexity science.
I’d like to raise a point that I didn’t in my remarks on Saturday, but builds on a discussion in a later session at the conference. One challenge that we often face as classical liberals is putting “the human face” on our ideas, countering the perception that a libertarian society would be cold, calculating, and lacking in compassion or personal relationships. Nothing is further from the truth, and the language of complexity science gives us a way to communicate that reality, because it frames economic/social interactions as relationships and connections. It emphasizes the mutuality of exchange and the multiple dimensions of our relationships with others in our voluntary associations.