Incentives and spillovers in R&D activities: an agency-theoretic analysis of industry-university relations

In this paper, I study industry-university relations in a principal- agent framework. Following the existing literature, these relations are interpreted in two ways: (1) as occurring through spillovers of knowledge among different groups of researchers, working for different institutional settings; or (2) as more formal interactions, through the possibility, for a scientist, to directly respond to incentives defined by the different communities she may belong to. I formalize these two configurations in a unified framework. I account for: (1) the inherent difficulty in measuring the impact of scientific activities; and (2) the multiplicity of activities that scientists perform. I combine multi-task agency models with distorted performance measures and common agency models. My model identifies several types of incongruities between an agent's actions and the desired outcomes. These incongruities derive also from the strategic interaction among the principals. I also identify some potentially distortionary behavioral effects of the presence of spillovers.Economics of Science; Agency Theory; Industry-University Relations; Science Policy; R&D Management.

Are managers indeed motivated by their bonuses?

Management;Incentives;labour economics

Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals

I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship. As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements

Economic Factors and Body Weight: An Empirical Analysis

With this study, we investigate the effects of changes in economic factors on body weight by constructing a utility theoretic model. The model is empirically estimated by combining data on individuals’ body weight, demographic and physical activity information, and statelevel measures pertaining to the prices of food away from home, food at home, and wages. By combining these data sources, we aim to estimate directly the weight effects of price and income changes. The empirical analysis suggests that decreasing the price of food at home could decrease body weight, a finding which has important public policy implications.body mass index, body weight, obesity, subsidy, tax, Agribusiness, Consumer/Household Economics, D11, D12, I18, H20, R22,

An Insider Misuse Threat Detection and Prediction Language

Numerous studies indicate that amongst the various types of security threats, the problem of insider misuse of IT systems can have serious consequences for the health of computing infrastructures. Although incidents of external origin are also dangerous, the insider IT misuse problem is difficult to address for a number of reasons. A fundamental reason that makes the problem mitigation difficult relates to the level of trust legitimate users possess inside the organization. The trust factor makes it difficult to detect threats originating from the actions and credentials of individual users. An equally important difficulty in the process of mitigating insider IT threats is based on the variability of the problem. The nature of Insider IT misuse varies amongst organizations. Hence, the problem of expressing what constitutes a threat, as well as the process of detecting and predicting it are non trivial tasks that add up to the multi- factorial nature of insider IT misuse. This thesis is concerned with the process of systematizing the specification of insider threats, focusing on their system-level detection and prediction. The design of suitable user audit mechanisms and semantics form a Domain Specific Language to detect and predict insider misuse incidents. As a result, the thesis proposes in detail ways to construct standardized descriptions (signatures) of insider threat incidents, as means of aiding researchers and IT system experts mitigate the problem of insider IT misuse. The produced audit engine (LUARM – Logging User Actions in Relational Mode) and the Insider Threat Prediction and Specification Language (ITPSL) are two utilities that can be added to the IT insider misuse mitigation arsenal. LUARM is a novel audit engine designed specifically to address the needs of monitoring insider actions. These needs cannot be met by traditional open source audit utilities. ITPSL is an XML based markup that can standardize the description of incidents and threats and thus make use of the LUARM audit data. Its novelty lies on the fact that it can be used to detect as well as predict instances of threats, a task that has not been achieved to this date by a domain specific language to address threats. The research project evaluated the produced language using a cyber-misuse experiment approach derived from real world misuse incident data. The results of the experiment showed that the ITPSL and its associated audit engine LUARM provide a good foundation for insider threat specification and prediction. Some language deficiencies relate to the fact that the insider threat specification process requires a good knowledge of the software applications used in a computer system. As the language is easily expandable, future developments to improve the language towards this direction are suggested