Essays on predictive and non-predictive strategies: real and simulated experiments

The dissertation studies how adopting a more systematic approach to decision-making impacts decision's outcomes and compares it with the adoption of non-predictive strategies. Coherently with previous literature (Camuffo et al. 2020), I call the systematic approach "scientific" since managers and entrepreneurs are asked to act as scientist would do in a business context. Indeed, this approach consists in developing theories and logic connections about the mechanism underlying future outcomes and test them with tailored experiments. "Scientific" managers and entrepreneurs are then called to analyse test results and make decisions accordingly. As mentioned, this approach helps decision-makers to improve their predictive power by probing the future with theory-based experiments but remains explorative. Managers and entrepreneurs explore other alternative ideas on which they can theorize on. On the non-predictive side, research on effectuation (Sarasvathy 2001; Dew et al. 2009; Chandler et Al. 2011) has shown how managers and entrepreneurs can deal with uncertainty by adopting a decision-making approach aimed to control the future instead of predicting it. Effectual decision-makers select alternative ideas based on loss affordability experimentation, and flexibility. But, in this case, experiments are not guided by well-framed theories and are not part of a systematic process. Sarasvathy (2008) uses the metaphor of a patchwork quilt: managers and entrepreneurs see the business context as a table where all the pieces are there but must be assembled or even created as the future is unpredictable. With the aim to unfold the mechanism driving diferent termination rates of ideas from the adoption of these two approaches, I propose a model with the aim to predict empirical results. The model proposes a Bayesian framework, where decision-makers acquire costly information to improve the precision of signals. Based on these informative signals, they act accordingly. I expect scientic decision-makers to react promptly to very informative bad signals. While I expect effectual decision-makers to react less to bad signals since they weight less predictive information. This translates into higher rates of termination for scientic decision-makers than effectual decision-makers. Moreover, scientic decision-makers terminate earlier than effectual decision-makers. In the second chapter, I focus on the scientific approach solely. I provide evidence of the implications of a scientific approach to decision-making through four Randomized Control Trials, involving start-ups and small-medium firms (SMEs) across two countries, Italy and UK. The three main findings are that scientic decision-makers are more likely to terminate their idea in early stages, corfiming findings of the previous chapter. They pivot fewer times before committing to one or terminate the idea. They also perform better in terms of revenues. A model has been developed to explain empirical results. In the third chapter, I study a way to scale research findings by using a simulation game to replicate, to some extent, results of the previous two chapters about the scientic approach

Entrepreneurial Action and Entrepreneurial Rents

This dissertation is comprised of three independently standing research papers (chapters 2, 3 and 4) that come together in the common theme of investigating the relationship between entrepreneurial action and performance. The introduction chapter argues that this theme is the main agenda of an entrepreneurial approach to strategy, and provides some background and context for the core chapters. The entrepreneurial approach to strategy falls in line with an array of action-based theories of strategy that trace their economic foundations to the Austrian school of economics. Chapters 2 and 3 take a game theoretical modeling and computer simulation approach and represent one of the first attempts at formal analysis of the Austrian economic foundations of action-based strategy theory. These chapters attempt to demonstrate ways in which formal analysis can begin to approach compatibility with the central tenets of Austrian economics (i.e., subjectivism, dynamism, and methodological individualism). The simulation results shed light on our understanding of the relationship between opportunity creation and discovery, and economic rents in the process of moving towards and away from equilibrium. Chapter 4 operationalizes creation and discovery as exploration and exploitation in an empirical study using data from the Kauffman Firm Survey and highlights the trade-offs faced by start-ups in linking action to different dimensions of performance (i.e., survival, profitability, and getting acquired). Using multinomial logistic regression for competing risks analysis and random effects panel data regression, we find that high technology start-ups face a trade-off between acquisition likelihood and profitability-given-survival while low and medium technology start-ups face a trade-off between survival and profitability-given-survival. Chapter 5 concludes the dissertation by highlighting some of the overall contributions and suggesting avenues for future research

Model-Based Reinforcement Learning for Stochastic Hybrid Systems

Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging robotic applications. However, such methods often obscure the structure of dynamics and control behind black-box over-parameterized representations, thus limiting our ability to understand closed-loop behavior. This paper adopts a hybrid-system view of nonlinear modeling and control that lends an explicit hierarchical structure to the problem and breaks down complex dynamics into simpler localized units. We consider a sequence modeling paradigm that captures the temporal structure of the data and derive an expectation-maximization (EM) algorithm that automatically decomposes nonlinear dynamics into stochastic piecewise affine dynamical systems with nonlinear boundaries. Furthermore, we show that these time-series models naturally admit a closed-loop extension that we use to extract local polynomial feedback controllers from nonlinear experts via behavioral cloning. Finally, we introduce a novel hybrid relative entropy policy search (Hb-REPS) technique that incorporates the hierarchical nature of hybrid systems and optimizes a set of time-invariant local feedback controllers derived from a local polynomial approximation of a global state-value function

DECISION SUPPORT FOR OPTIMAL WELL PLACEMENT, INFRASTRUCTURE INSTALLATION AND PRODUCTION PLANNING IN OIL FIELDS

Ph.DDOCTOR OF PHILOSOPH

There is nothing more practical than a good theory to make strategic decisions: evidence from field experiments in developed and developing countries

A theory-based approach to strategic decision-making under uncertainty recommends decision-makers and entrepreneurs to formulate a theory behind their decision problems and to follow a structured framework to make decisions. Theoretical reasoning should enable actors to generate more comprehensive representations of the world, ground hypothesis testing in a clear framework, identify causal mechanisms and better interpret results from experimentation efforts. However, despite the abundance of theoretical research, limited empirical evidence of the effectiveness of such systematic approach to decision-making is available. Most of the evidence in entrepreneurial settings focuses on decision outcomes and performance, with few papers investigating mechanisms or intermediate outcomes that can affect the decision-making process. This thesis contributes to the stream of research on theory-based approaches in entrepreneurial decision-making, and more generally to the literature about decision-making under uncertainty, by 1) proposing novel theoretical arguments for the channels through which a theory-based approach improves decision outcomes; 2) providing novel empirical evidence on the matter leveraging three distinct field experiments conducted with entrepreneurs in both developed and developing countries, the latter being a context widely understudied in the strategy literature. Particularly, the three chapters are devoted to the examination of entrepreneurs’ perceptions and their ultimate connection to outcomes, analyzing whether and how they are affected by the application of a theory-based approach to decision-making. Entrepreneurs’ perception of their ideas, the environment in which they act, as well as self-perceptions in relation to that environment are important mechanism that despite their importance for decision-making processes have not been widely studied yet in the literature. The goal of this thesis is to disentangle the effects that following a theory-based approach has on different dimensions of entrepreneurs’ perceptions and ultimately on business outcomes. Each chapter studies different aspects of the decision-making process and different types of perceptions. The first chapter develops a theoretical framework explaining how a theory-based approach affects entrepreneurs’ perceptions of their projects’ value, and how this change in perceptions leads to a better selection process with respect to entrepreneurs not following a structured approach to decision-making. Results also show how this process ultimately results in better business outcomes for entrepreneurs following a theory-based approach. The second chapter compares again these two groups of entrepreneurs, focusing on pivoting activities and business model changes. Results show how entrepreneurs following a theory-based approach introduce changes that are more customer-centric, and how this leads to a different update process on their beliefs about project’s value and related uncertainty. The third chapter leverages the unique setting of an emerging economy, Tanzania, to study entrepreneurs’ perceptions of ability to deal with potential challenges to business development and how they relate to performance and uncertainty perceptions. Results show how entrepreneurs trained to follow a theory-based approach perceive themselves as better able to deal with potential challenges when compared to entrepreneurs trained with a structured approach solely based on experimentation, ultimately isolating the positive spillovers related to the theoretical element of decision-making

経営者の主観的要因と企業行動

早大学位記番号:新8237早稲田大

Essays on the impact of different forms of collaborative R&D on innovation and technological change

R&D alliance is a multifaceted phenomenon, in which various socio-technological mechanisms operate in the interaction of partner firms. This dissertation is composed of three studies to shed light on different dimensions of firms’ resources and performance in different forms of R&D collaborations. These studies consider (1) how the partner firms differences with respect to different dimensions of their knowledge bases influence inter-firm learning in dyadic R&D alliances, (2) how the partner firm differences in their resources across locales influence the multi-partner alliance performances at both alliance and firm levels, and (3) how firms leverage R&D collaboration to navigate the dynamics of technology selection during technology change. The findings of these studies tie together to the extent that they clarify the complex dynamics that exist between individual firms and their alliance partners in order to realize individual and joint value. In general, this dissertation contributes to the strategy and technology management literature by elucidating the less-explored dimensions of the firm’s resources and performance in R&D collaborations

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system