The Complexity of the Firm

In 1937, a year after Turing had his seminal paper, Ronald Coase published a paper The Nature of the Firm to give a framework to why we have companies and how large they become. In a perfect market economy we shouldn't need a firm at all, everyone is just an independent contractor and market pricing will drive efficient use of labor. Coase notes there are costs to creating contracts and one can gain efficiencies by avoiding these contracts by having both parties inside the same organization.

Much of those costs come from complexity, it is impossible to create a contract to cover all possible outcomes and thus contracts are incomplete leading to loopholes and lawsuits.

In the other direction, having the central organization of a firm has its own costs, from inefficiencies from not using markets to balance supply and demand, to the complexity of the organization processes themselves. In Coase's model, a firm grows to a size that balances the organization and contract costs at the margin.

So what happens to the sizes of firms when we reduce complexity, as happens with modern computing, from better communication, optimization and data analytics. We see relatively new companies like Google and Amazon getting larger. We also see a number of startups and small companies with very few workers, sometimes just one.

Computing drops both the cost of central organization and the cost of contracts so the size of a firm depends on circumstance. One can have a tech startup with a small number of workers since they can write apps that run on other people's platforms (like web browsers and on phones) and have the processing done on the cloud where they can scale or not scale as needed. Meanwhile a large company can more easily coordinate and connect using modern technology making it cost efficient to expand.

As we enter a new age of machine learning and automation, how will that affect the very nature of companies in the future? How about universities, a special kind of firm in itself? How can we harness the tools and ideas from computational complexity to help understand these and other societal changes.