Events

On 28 and 29 November the “Artificial Intelligence & Intellectual Property Conference” will take place in Singapore. The event, organized by the School of Law of the Singapore Management University (SMU) together with the Max Planck Institute for Innovation and Competition and the Law Faculty of the Chinese University of Hong Kong (CUHK), brings together academics, policy makers, lawyers and practitioners. The program focuses on the immediate legal impact of AI in different countries. The conference will discuss not only theoretical approaches, but also the implementation of new technology-oriented laws and guidelines for regulators considering an amendment to their IP laws.

See Conference Website

Max Planck Institut for Innovation and Competition, Munich, Room 313

The increasing use of algorithms in legal and economic decision-making has led to calls for a “right to explanation” to be given to the subjects of automated decision-making. A growing literature in computer science has proposed a vast number of methods to generate such explanations. At the same time, legal and social science scholars have discussed what characteristics explanations should have to make them legally and ethically acceptable. These debates suffer from two shortcomings. First, very little connection exists between these two strands of literature. Second, we do not know what effects such explanations would have on the behavior of decision subjects and on their perception of decision-making algorithms. In this field experiment, we aim to address these gaps by empirically testing how different types of explanations affect the subjects’ attitude towards decision-making algorithms. Distilling various factors that constitute a good explanation of algorithmic decision-making, we collect data on which factors are useful to decision subjects: local or global explanations, explanations which are selective, contrastive and/or are displayed as conditional control statements versus correlations. In the setting of a scholarship awarded by a machine learning algorithm to promising students, our experiment thus investigates which kind of explanations can lead to increased acceptance of algorithmic decision-making.

Contact Person: Dr. Marina Chugunova

The phenomena connected to the development of smart urban mobility solutions raise issues in various legal fields pinpointing the interaction between technology, law, and public policy. This complex relationship can be tackled from different angles of analysis: the public sector perspective, the private sector perspective, and the citizens’ perspective.

Max Planck Institute for Innovation and Competition, Munich, Room E10

Professor Wang introduces a theory of intelligence, a formal model of the theory, and a computer implementation of the model. He takes “Intelligence” as the ability of adaptation under insufficient knowledge and resources. Non-Axiomatic Reasoning System (NARS) is a formal model realizing this theory, which has been implemented in an open source project OpenNARS. The system shows many properties observed in the human mind. Practical applications of this technique are also under development.

Max Planck Institute for Innovation and Competition, Munich, Room 313

Strategic behavior can not only affect effort in contests but also undermine their selection function. We investigate two forms of strategic behavior of contestants with heterogeneous ability in large contests: Sabotage and self-promotion. We test predictions from a simple theoretical model in a large dataset of more than 38 million peer-ratings by 75,000 individuals. We find a) that strategic behavior influences outcomes in 25% of close contests, b) that self-promotion is the dominant form of strategic behavior of low-ability contestants, and c) that high-ability contestants are both culprits and targets of sabotage. We leverage two natural experiments to rule out alternative explanations.

Contact Person: Klaus Keller, M.A.

Max Planck Institute for Innovation and Competition, Munich, Room 313

Japan counts as the third largest market for medical devices after the US and the EU, and displays an exceptionally high number of certain technologies per capita (e.g. CT and MRI). Surprisingly, most appliances are imported to Japan nowadays pointing to a drop in innovation activities since the 1990s. A change can be observed rather recently under PM Abe by integrating the field of medical devices into the scheme of the Japan Revitalisation Strategy (Abenomics), which aims at “renkei” ni yoru “jitsuyōka” (market cultivation through partnerships) between medical centres, academia and manufacturing companies (METI 2016). Against this backdrop, this paper sheds light on which factors lead to this situation by focusing particularly on disciplinary boundaries. What is more, the presentation highlights current efforts on medtech partnerships, cluster policies and matching-hubs to cross these boundaries and to encourage innovation activities in the field of medical devices in Japan. The paper mainly draws on insights from research literature and preliminary findings from two case studies. Based on these findings it can be said that regional authorities and municipalities promote R&D activities by offering subsidies to small and medium-size enterprises (SME) and organising matching-hubs for ikō renkei (medtech partnership) such as in Tokyo, Kobe, Kyushu, Fukushima and Shizuoka but with varying degrees of success.

Contact Person: Dr. Marina Chugunova

Max Planck Institute for Innovation and Competition, Room E10

Moderation: Jörg Hoffmann

Max Planck Institute for Innovation and Competition, Munich, Room 313

Estimates of the private and social rates of return to investments in R&D are of high interest to economists, managers and policymakers. An important problem in the literature is that the canonical model used to obtain such estimates only allows R&D to diffuse through spillovers. This is a serious limitation in a world increasingly characterized by active intellectual property (IP) enforcement and monetization. We create a new dataset of interactions in the market for technology between publicly held firms in the U.S. which allows us to generalize the canonical model with both spillovers and market-mediated technology transfers. We obtain four main findings using changes in tax incentives for R&D to identify causal effects. First, R&D accessed through technology markets is an important input in the generation of revenue. Second, conventional spillover estimates are contaminated with technology transfers because the weights traditionally used to capture spillovers are strongly correlated with matching in the market for technology. Third, the private rate of return to R&D is larger in the generalized framework while the wedge between the social and private returns is smaller. Finally, back of the envelope estimates suggest that the gains from trade in the market for technology might be larger than $1 trillion per year, accounting for 10% of total revenue in Compustat.

Contact Person: Dr. Rainer Widmann

Max Planck Institute for Innovation and Competition, Munich, Room 313

An increasing number of research projects successfully involves the general public (the crowd) in tasks such as collecting observational data or classifying images to answer scientists’ research questions. Although such crowd science projects have generated great hopes among scientists and policy makers, it is not clear whether the crowd can also meaningfully contribute to other stages of the research process, in particular the identification of research questions that should be studied. We first develop a conceptual framework that ties different aspects of “good” research questions to different types of knowledge. We then discuss potential strengths and weaknesses of the crowd compared to professional scientists in developing research questions, while also considering important heterogeneity among crowd members. Data from a series of online and field experiments has been gathered and is currently analyzed to test individual- and crowd-level hypotheses focusing on the underlying mechanisms that influence a crowd’s performance in generating research questions. Our results aim for advancing the literatures on crowd and citizen science as well as the broader literature on crowdsourcing and the organization of open and distributed knowledge production. Our findings have important implications for scientists and policy makers.

Contact Person: Michael E. Rose, Ph.D.