Searching for Needles in a Haystack: Three Essays on the Role of R&D Partnerships in the Bio-Pharmaceutical Industry

This dissertation examines the relationships of established and young startup firms in environments characterized by rapid technological change in which exploration, i.e., moving away from current organizational routines and knowledge bases, is crucial for success. In the first essay, I combine perspectives on organizational myopia and organizational learning to examine how prior successes and prior failures solving R&D problems shape whether established firms go beyond local search in partnerships formations with startup firms. In line with the myopia perspective, I find that established firms tend to overlook partnering opportunities with novel elements of knowledge as well as opportunities that do not promise payoffs in the immediate future. The study further reveals that prior successes and failures very differently shape these myopic tendencies. While prior failures lead firms to pursue partnerships with novel elements of knowledge, prior successes make firms more receptive to partnerships with payoffs in the distant future. In the second essay, I draw on the literature on organizational learning and inter-organizational partnering to examine the relationship between startup innovation and startup market valuations. I find that startups pursuing innovations that substantially differ from the solutions pursued by established firms may face severe market value penalties as they lack both legitimacy and access to vital complementary assets. This penalty, however, is attenuated if startups commercialize their innovations through partnerships or if they pursue innovations in areas where established firms have failed in their own internal R&D attempts. The final essay draws on the literature on inter-organizational learning to more closely examine the ways in which established firms leverage the knowledge accessed from startup firms. I focus on loosely coupled partnerships that involve established firms paying a research partner for access to specific knowledge. While prior research has questioned the ability of firms to devise new and innovative solutions based on such partnerships, I find that innovation benefits from loosely coupled partnerships do not necessarily stem from the sourcing relationship per se but instead are contingent on the established firm\u27s experimental orientation to pursuing risky projects and its availability of financial and managerial resources. Overall, my dissertation enriches our understanding of the unique interdependencies between established and startup firms in environments characterized by rapid technological change

Reset-free Trial-and-Error Learning for Robot Damage Recovery

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at https://youtu.be/IqtyHFrb3BU, code at https://github.com/resibots/chatzilygeroudis_2018_rt

Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability