![]() ![]() Relying solely on heuristic analysis creates a number of potential dangers for VCs and the companies they work with. 2 Most people rely on heuristics to simplify the complex task of assessing probabilities and forecasting value when faced with uncertainties, but while “in general, these heuristics are quite useful…sometimes they lead to severe and systematic errors.” 3 In this business, it is too easy to confuse luck with skill.Ĭognitive psychologists Daniel Kahneman and Amos Tversky have shown that humans are prone to thinking errors-cognitive biases-when assessing uncertain events or making decisions in the face of uncertainty. Few data points and long feedback loops make for slow learning, while the extreme uncertainty makes it difficult to learn the right lessons. The average venture capitalist only makes a few decisions a year, and it takes years to see if these decisions result in good or bad outcomes. To make matters worse, venture is a terrible learning environment. The most promising markets tend to change quickly and domain expertise becomes obsolete just as quickly. Information tends to be incomplete and unreliable. Most startups involve some combination of unproven technologies, inexperienced teams, undeveloped markets, and untested business models. Venture is a tough business that requires meaningful commitments of resources in the face of extreme uncertainty and dynamic markets. The Inherent Difficulty of Venture Decisions I conclude with a consideration of the implications of decision analysis for the venture industry. The main discussion illustrates how we at Ulu Ventures, where I am a co-founder and partner, applied this approach to an early-stage investment in Inkling, an interactive textbook platform for the iPad. This article opens by examining the challenges of venture decision-making and describing how a decision analytic approach can overcome these challenges to produce more informed decisions. I found the similarities compelling, and with my academic background in decision analysis (including a PhD from Stanford) and 20 years of experience applying it to a wide range of problems and industries, I was well prepared to develop a decision-analysis framework for venture investing. All three industries require large amounts of initial capital, face significant uncertainty, and achieve success (if ever) years after the original investment. The framework I adopted to improve my investment judgment was decision analysis, a rigorous and sophisticated set of tools that have been adopted as best practice in industries analogous to venture, such as pharmaceutical research and development and upstream oil and gas exploration. When I started my career in private equity I knew I would have my share of experiential education through failed investments, but I also believed I could shorten my learning curve and-by finding a way to make smarter decisions-possibly limit the tuition costs of becoming a venture investor. 1 Failure may be the best teacher, but failure in early-stage investing comes at a high cost. Vinod Khosla once said it takes seven years and $30 million to train a venture capitalist (VC).
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