Posted on May 6, 2016 @ 09:35:00 PM by Paul Meagher
I never really gave much thought to the practical importance of the philosophical distinction between correspondence and coherence theories of truth until I read Kenneth Hammond's book Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice (1996). It turns out that in research on judgment and decision making the distinction is very important because it defines what researchers consider "good" or "correct" judgment and decision making. For someone subscribing to a coherence theory of truth, the truth of a statement is determined by how well it fits with other things we take to be true, such as probability theory.
Nobel Laureate Daniel Kahneman in his best-selling book Thinking, Fast and Slow (2011) discusses a variety of experiments that purport to demonstrate how poorly humans often reason because their reasoning does not accord with the rules of probability theory. The experiments demonstrate many different types of biases (anchoring, framing, availability, recency, etc...) that human reasoning is subject to based on their disagreement with the rules of probability theory.
Professor Kenneth Hammond, and before him, his mentor Egon Brunswik, were not big fans of the coherence theory of truth. They preferred a correspondence theory where the truth of a statement is determined by whether it corresponds to the facts. They believed that our access to the facts was often mediated by multiple fallible indicators.
We may not be able to verbalize some of the indicators we use in our judgment, or how we are combining them, but our intuitive understanding can lead to accurate judgments about the world even if we don't have a fully coherent account of why we believe what we do. Often the judgment rule turns out to be a simple linear model that combines information from multiple fallible indicators. Experiments in this tradition involve people making judgments about states of the world based upon indicator information and examining the accuracy of their judgments, the ecological validity of the indicators, and whether judges utilize the indicator in a way that corresponds to its ecological validity.
So how you conduct and interpret experiments in judgment and decision making are affected by whether you believe correspondence theories of truth are superior to coherence theories of truth and vice versa. They are metatheories that determine the specific theories we come up with and how we study them.
The distinction is relevant to entrepreneurship. For example, a business plan is arguably a document designed to present a coherent account of why the business will succeed. If you've ever questioned the value of a business plan it could be because it is a document that is judged based upon coherence criteria but the actual success of the startup will depend upon whether the startup's value hypothesis and growth engine hypothesis corresponds with reality. Eric Ries, in his best selling and influential book, The Lean Startup (2011) discussed many techniques for validating these two hypothesis. Although he does not discuss the correspondence theory of truth as his metatheory, it is pretty obvious he subscribes to it.
In practice, the correspondence theory of truth often involves defining and measuring indicators and making decisions based on these indicators. In the lean startup, Eric Ries advocates looking for indicators to prove that your value hypothesis is true. If the measured indicators don't prove out your value hypothesis you many need to start pivoting until you find a value hypothesis that appears correct according to the numbers. If your value hypothesis looks good, then you will need to validate your growth hypothesis by defining and measuring key performance indicators for growth. The lean startup approach is very experiment and measurement driven because it is a search for correspondence between the value and growth hypothesis and reality.
We can represent the lean startup value and growth hypothesis with a lens model by making a slight modification to Kenneth Hammond's version of the Lens Model:
This diagram should actually be two lens models, one for the value hypothesis and one for the growth hypothesis. I'm being lazy. The lens model for the value hypothesis asks what indicators can we use to measure whether our product or service delivers the value that we claim it does. The lens model for the growth hypothesis asks what indicators we can use to measure whether our growth engine is working. You should read the book if you want examples of how indicators of value and growth were defined, measured and used in the various startups discussed.
One reason why the lean startup theory is useful is because success in starting a business is defined more in terms of correspondence with reality than coherence with other beliefs that we might hold to be true. There are lots of situations where the coherence theory of truth might be useful, such as narratives about the meaning of life and social interactions where truth is a matter a perception and plausible story telling, but that does not get you very far if you are a startup or running a business. Correspondence is king.
If correspondence is king, you might find the lean startup lens model above offers a simple visualization that can be used to remind you of how accurate judgments regarding the value and growth hypothesis for startups are achieved.