In the previous modules, we have seen that the road towards innovation and value creation is riddled with obstacles, because the problem-solving processes underlying decision-making are characterized by uncertainty. These module illustrates the concepts of risk and uncertainty in entrepreneurial and managerial decision-making, leveraging on the classic contribution of Frank Knight, who distinguishes three types of uncertainties about the future that entrepreneurs and managers may face. The first consists of a future whose possible states of the world exist. Their probability distribution is known, and, therefore, decisions would only involve calculating the odds of a particular draw and placing one's bets based on the analysis. In this case, decisions are similar to a lottery. One should pick the option with the highest expected value, and the risks can be reduced through diversification. The second consists of a future whose possible states of the world exist, but their probability distribution is not known in advance. The entrepreneur or manager, in this case, has to estimate the distribution, that is the probabilities, through trials or data, and can then treat the decision the same as in the first case. As the environment changes dynamically, however, different states of the world and options might become available, and new estimates of the probability distributions are necessary. Careful experimentation and learning might help in that direction. Although entrepreneurs and managers do not know the probabilities attached to each of the outcome scenarios, these probabilities do exist, and their distribution can be uncovered, with effort, overtime. The third type of uncertainty, which Knight called ‘true uncertainty’, consists of a future that is not only unknown, but also unknowable, with unclassifiable states of the world and a non-existent distribution of probabilities. These three different types of uncertainty are associated with different types of problem-solving and decision-making processes. In the first case, innovation opportunities are recognized, as all the elements are known and the issue is calculating the optimal solution. In the second case, innovation opportunities are discovered, as problems and solutions change overtime, as well as their probabilities distributions. In the third case, innovation opportunities are created, as problems and solutions are endogenous. That is, they are created by the economic actors themselves. In order to appreciate what this means in practice, let's consider the case of PayPal. Born in Ukraine, in Kiev, and moved to Chicago in 1991, Max Levchin began to take an interest in security and encryption technologies as a young computer scientist. He developed an application on PalmPilot, which replaced the existing solutions at the time used to generate passwords. He decided to move to Silicon Valley to improve and extend his idea, but changed his mind when he met Peter Thiel and Luke Nosek, two venture capitalists, who convinced him that the internet boom and the net economy would transform financial transactions, requiring the use of encryption to make secure online payments. They founded together a company, Confinity Inc., exploring software applications for data security, digital wallets, and peer-to-peer money transfers among handheld devices. During this process, they met Elon Musk, who had launched his online bank, X.com, but found Levchin and Thiel's idea, secure financial transactions, online P2P, much more interesting. Confinity merged with X.com, eventually becoming PayPal. PayPal was then acquired by eBay in 2002, becoming a worldwide success, and one of the world's most used payment systems. The history of PayPal allows to better explain what it means to decide under uncertainty. At first glance, the initial situation facing Levchin, seems to be that of a decision under conditions of risk. There was a problem: data security on handhelds. There was a solution: a software to encrypt. There were clearer possible alternative states of the world: the handhelds will spread and data security will be an issue, versus the handhelds do not spread and security won't be a problem. Probabilities are known. Everybody says it's highly likely that the handhelds will spread and that data security will most likely be a critical issue and widespread. In this case, Levchin could decide as in a lottery, or in front of the toss of a coin. Calculate the value associated with the decision to pursue a business idea, expected value based on the probability, versus not pursue it. However, this was not the situation in which Levchin found himself. In the late 1990s, handhelds were not so prevalent, and they were very different from the current smartphones, with much fewer functionalities and features. So, it was really hard to predict whether they would have kept on growing. Furthermore, it was also hard to tell if data security on handhelds would be a problem or not. Experts had different opinions about to what extent the palmars would diffuse and develop. Consequently, the probability distribution of the above-described states of the world were not really known, as it usually happens. The founders of PayPal were clearly deciding under conditions of uncertainty. Consequently, the challenge that Levchin and partners were faced with was first to discover such probability distributions through estimation procedures. Once estimated through information and data gathering and analysis, the same approach previously described could be applied. However, as Levchin, Thiel and associates collected information, analyzed data, and updated their estimates of probability, also the states of the world changed, requiring further estimates and significant change in the decision-making context and process. Levchin, Thiel, Nosek, Howery, and Musk were clearly under conditions of uncertainty. Indeed, they found themselves in an even more complex situation. Not only the possible states of the world had unknown probabilities and changed over time with the implication that estimates were increasingly complex to make, but also the customer problem they were trying to solve was itself wicked, ambiguous, and changing. Initially, based on the fact that Levchin was a computer scientist, the teamwork on how to secure information on PDAs, as they thought it was an important issue for all potential users of PDA devices. But then, moving to Silicon Valley, they shift increasingly towards peer-to-peer secure payment systems. During this process of reorientation, Levchin and associates not only updated their estimates of probability distributions related to the possible states of the world on existing options, but also modified both the solutions, technologies, and - especially - the definition of the problem they wanted to solve. For example, they changed the target customer. As Nathan Furr and Jeff Dyer showed in their book, ‘The Innovator's Method’, it took five strategic iterations, or pivots, to get eventually to the PayPal we know today. The example of PayPal offers a new perspective on the role that entrepreneurs and managers perform when trying to innovate. They don't simply try to reduce the uncertainty by building better solutions or options, collecting more information and analyzing them better. Rather, they also try to influence the context in which they operate. For example, by forging new problems and persuading the customers that they have it and that they need to solve it. They create languages, logical categories, and theories, urging awareness and emotions towards phenomena, ideas, and events. In other words: problems, solutions, and their combinations, which we define as innovation opportunities, are not only subject to recognition and discovery, but also created. This creative activity is generally bound by the resources available to managers and entrepreneurs, their preferences - in terms of ambiguity and risk - and their cognitive styles. Summarizing, entrepreneurs and managers recognize, discover, or create innovation opportunities, problems which - if solved - create value. They have to validate problems and solutions, and then evaluate if - and to what extent - these pairings generate value.
