Alberto López

Understanding co-operative innovative activity: evidence from four European countries

We investigate co-operative innovative activity in four major European countries, France, Germany, Spain and the UK, using internationally comparable firm-level data for manufacturing and service sectors. We examine the roles of knowledge flows, cost- and risk-sharing and public financial support in firms’ decisions to collaborate. Our results suggest that firms which place greater value on external information flows are more likely to co-operate with the research base than with other firms and that firms facing appropriability problems are more likely to co-operate with the research base and with upstream and downstream firms than with direct competitors. We find evidence for Spain to suggest that firms collaborate to overcome risks and financial constraints. We also find that receipt of public support is positively related to undertaking collaborative innovation. In line with the focus of policy, this relationship is strongest for co-operation with the research base.

Programming Robosoccer agents by modeling human behavior

The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.

Determinants of Abandoning Innovative Activities: Evidence from Spanish Firms

Abstract We empirically analyse the effect of firms’ barriers to innovation on the probability of abandoning innovation projects. Specifically, we study factors related to the availability of finance, knowledge and market conditions. We use a sample of more than 8,300 innovative Spanish firms for the period 2005–2007. We find that all obstacles to innovation have a positive effect on the probability of abandoning innovative activities. Results show that market-related factors seem to be the most important determinants of innovation failure. Analyzing results by firm size, we find that, for small-medium firms, the main factors that lead to abandoning innovative projects are competition from established firms and market uncertainty. In contrast, for large firms, the most important barriers are the lack of qualified personnel and the availability of external finance. These results might suggest that large and small-medium firms differ in the scale and complexity of their innovative projects.

Assessing the impact of domestic outsourcing and offshoring on productivity at the firm level

The purpose of this article is to investigate the effects of domestic and foreign sourcing on firm-level productivity. We develop a simple framework that permits the introduction of both domestic outsourcing and offshoring in a conventional production function. We find that the decision to outsource has a positive impact on the level of productivity as it permits the relocation of parts of the production process to external suppliers. Furthermore, there is a positive impact on output resulting from domestic outsourcing and offshoring like any other input.

Sourcing strategies and productivity: evidence for spanish manufacturing firms

This paper explores the relationship between productivity and sourcing strategies using a sample of Spanish manufacturing firms. In this analysis we use different measures of productivity and alternative ways of grouping firms. Our results indicate that productivity differs systematically across groups of firms with different sourcing strategies. The ranking of productivities we observe is not fully consistent with the predictions of Antràs and Helpmans (2004) model but we confirm many partial aspects of the proposed ranking. In our preferred specifications, the productivity advantage is highest for firms that import inputs from foreign subsidiaries. Moreover, the group of firms that outsource in the domestic market is, in general, at the bottom of the productivity distribution, with firms that outsource abroad having an intermediate productivity advantage. When analysing the direction of causality between productivity and sourcing strategies, we find weak evidence of self-selection of firms into offshoring and no influence of relocation on firm productivity.

The complementarity effect of research and development on firm productivity

Returns to investment in research and development (R&D) are a subject of considerable interest in the literature on economics of innovation. However, a drawback of this literature is that R&D is mostly treated as a homogeneous activity. This article analyses the differentiated effect of research and development on productivity and tests the existence of complementarity between these activities. We find evidence supporting the existence of a direct effect of both innovation activities. Most interestingly, our results suggest that there is complementarity between research and development in determining productivity.

Using Hierarchical Knowledge Structures to Implement Dynamic FAQ Systems

This paper presents a knowledge representation that allows building hierarchical structures that can be used to build dynamically solutions to solve a particular user question. The goal of this work is to show how it is possible to define (and handle) a new knowledge representation that allows the integration of Case-based Knowledge into a graph-based representation that can be easily learned and managed. The combination of both, Case-based Knowledge and graphs allows to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The paper shows how the management of these cases allow enriching the knowledge base. Finally, the paper shows a specific application of this technique, in particular a Web system named DynJAQ (Dynamic Java Asked Questions). DynJAQ is a FAQ system that is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java).

Empirical models of firms’ R & D: Applications

This paper is the chapter 18 of the Handbook of Game Theory and Industrial Organization (Vo. II). This chapter is devoted to the empirical models of firms’ R&D. This literature is still growing due to the increasing availability of micro-data. Taking this fact into account, the main purpose of this chapter is to provide an overview of three important topics covered by the recent literature: the determinants of firms’ R&D investment, the link between R&D, innovation and productivity, and the analysis of the R&D black box. This chapter is presented as an invitation to industrial organization practitioners, both theorists and applied, to cross the bridge (and to change sides) between theory and applications.

Corrigendum Programming Robosoccer agents by modeling human behavior [Experts Systems with Applications 36 (2P1) (2009) 1850-1859]

Corrigendum ‘‘Programming Robosoccer agents by modeling human behavior” [Experts Systems with Applications 36 (2P1) (2009) 1850–1859] Ricardo Aler , Jose M. Valls , David Camacho *, Alberto Lopez a,1 Computer Science Department, Universidad Carlos III de Madrid, Avenue Universidad, No. 30, 28911 Leganes, Madrid, Spain Computer Science Department, Universidad Autonoma de Madrid, C/Francisco Tomas y Valiente, No. 11, 28049 Madrid, Spain