Companies innovating in dynamic environments face the combined challenge of unforeseeable uncertainty (the inability to recognize the relevant influence variables and their functional relationships; thus, events and actions cannot be planned ahead of time) and high complexity (large number of variables and interactions; this leads to difficulty in assessing optimal actions beforehand). There are two fundamental strategies to manage innovation with unforeseeable uncertainty and complexity: trial and error learning and selectionism. Trial and error learning involves a flexible (unplanned) adjustment of the considered actions and targets to new information about the relevant environment as it emerges. Selectionism involves pursuing several approaches independently of one another and picking the best one ex post. Neither strategy nor project management literatures have compared the relative advantages of the two approaches in the presence of unforeseeable uncertainty and complexity. We build a model of a complex project with unforeseeable uncertainty, simulating problem solving as a local search on a rugged landscape. We compare the project payoff performance under trial and error learning and selectionism, based on a priori identifiable project characteristics: whether unforeseeable uncertainty is present, how high the complexity is, and how much trial and error learning and parallel trials cost. We find that if unforeseeable uncertainty is present and the team cannot run trials in a realistic user environment (indicating the project's true market performance), trial and error learning is preferred over selectionism. Moreover, the presence of unforeseeable uncertainty can reverse an established result from computational optimization: Without unforeseeable uncertainty, the optimal number of parallel trials increases in complexity. But with unforeseeable uncertainty, the optimal number of trials might decrease because the unforeseeable factors make the trials less and less informative as complexity grows.innovation, project management, complexity, unforeseeable uncertainty, selectionism, learning, NK-model, traveling salesman problem, ambiguity

N ovel startup companies often face not only risk, but also unforeseeable uncertainty (the inability to recognize and articulate all relevant variables affecting performance). The literature recognizes that established risk planning methods are very powerful when the nature of risks is well understood, but that they are insufficient for managing unforeseeable uncertainty. For this case, two fundamental approaches have been identified: trial-and-error learning, or actively searching for information and repeatedly changing the goals and course of action as new information emerges, and selectionism, or pursuing several approaches in parallel to see ex post what works best. Based on a sample of 58 startups in Shanghai, we test predictions from prior literature on the circumstances under which selectionism or trial-and-error learning leads to higher performance. We find that the best approach depends on a combination of uncertainty and complexity of the startup: risk planning is sufficient when both are low; trial-and-error learning promises the highest potential when unforeseeable uncertainty is high, and selectionism is preferred when both unforeseeable uncertainty and complexity are high, provided that the choice of the best trial can be delayed until its true market performance can be assessed.

International audienceSince Osborn's Applied Imagination book in 1953 (Osborn, A. F. 1953. Applied Imagination: Principles and Procedures of Creative Thinking. Charles Scribner's Sons, New York), the effectiveness of brainstorming has been widely debated. While some researchers and practitioners consider it the standard idea generation and problem-solving method in organizations, part of the social science literature has argued in favor of nominal groups, i.e., the same number of individuals generating solutions in isolation. In this paper, we revisit this debate, and we explore the implications that the underlying problem structure and the team diversity have on the quality of the best solution as obtained by the different group configurations. We build on the normative search literature of new product development, and we show that no group configuration dominates. Therefore, nominal groups perform better in specialized problems, even when the factors that affect the solution quality exhibit complex interactions (problem complexity). In cross-functional problems, the brainstorming group exploits the competence diversity of its participants to attain better solutions. However, their advantage vanishes for extremely complex problems

Designing incentive contracts that constructively guide employee efforts is a particularly difficult challenge in novel innovation initiatives, where unforeseen events may occur. Empirical studies have observed a variety of incentive structures in innovation settings: "time and material contracts" (compensation for executing orders), "downside protection" (target-driven incentives with protection from unexpected risks), and "upside rewards" (additional remuneration for pursuing opportunities). This paper develops a model of incentives in presence of unforeseen events and offers a theoretical prediction of which of the empirically observed incentive structures should be used under which circumstances. The combination of three key influences drives the shape of the best incentive contract. First, the presence of unforeseeable uncertainty, or the occurrence of events that cannot possibly be foreseen at the outset. These may force a change in the project's plan, making pure target setting insufficient. Second, fairness concerns dictate that the employee's expected compensation cannot be shifted downward by unforeseen events, because it would cause demotivation, hostility, and defection. Third, management may not be able to observe the detailed actions of the employee (moral hazard) nor whether a positive or negative unforeseen event has occurred (asymmetric information)

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