Product and Process Innovation in a Growth Model of Firm Selection

Recent empirical evidence based on firm level data emphasizes firm heterogeneity in innovation activities and the different effects of process and product innovations on the productivity level and productivity growth. To match this evidence, this paper develops an endogenous growth model with two sources of firm heterogeneity: production efficiency and product quality.Both attributes evolve endogenously through firms’ innovation choices. Growth is driven by innovation and self-selection of firms and sustained by entrants who imitate incumbents. Calibrating the economy to match the Spanish manufacturing sector, the model enables to quantify the different effects of selection, innovation, and imitation as well as product and process innovation on growth. Compared to single attribute models of firm heterogeneity, the model provides a more complete characterization of firms’ innovation choices explaining the partition of firms along different innovation strategies and generating consistent firm size distributions.endogenous growth theory, firm dynamics, heterogeneous firms, productivity, quality, innovation

Technological knowledge and the theory of the firm: The role of idiosyncratic factors in the quest for the economics of distinctive competences

This paper elaborates a theory of the firm that combines the intuitions of Edith Penrose with the analysis of localized technological knowledge. The analysis of the characteristics of knowledge indivisibility and of idiosyncratic factors pIay a key role in shaping the intentionai strategy of firms about the direction of technology strategies. The firm is viewed as a Iearning agent that, induced by market forces and buiIding upon Iearning processes, elaborates and impiements intentionally strategies of knowledge generation. These strategies include the necessary identification of the externai sources of compiementary technoiogicai knowledge and of the idiosyncratic production factors that is convenient to lise intensiveIy. Learning, in fact is a necessary, but not sufficient condition for the generation of new knowledge. The anaIysis of the conditions for the intentional generation of technoiogicai and organizationai knowledge becomes crociato The analysis of the combined effects of internai Iearning, externai knowledge and intensive lise of idiosyncratic factors by means of the introduction of biased technological change CUlli intentional decision­making provides key inputs to understanding the path dependent and idiosyncratic features of the knowledge generated by the firm as the basis for its distinctive competences.

Opinion Polarization by Learning from Social Feedback

We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017

Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations

In many real-world imitation learning tasks, the demonstrator and the learner have to act in different but full observation spaces. This situation generates significant obstacles for existing imitation learning approaches to work, even when they are combined with traditional space adaptation techniques. The main challenge lies in bridging expert's occupancy measures to learner's dynamically changing occupancy measures under the different observation spaces. In this work, we model the above learning problem as Heterogeneous Observations Imitation Learning (HOIL). We propose the Importance Weighting with REjection (IWRE) algorithm based on the techniques of importance-weighting, learning with rejection, and active querying to solve the key challenge of occupancy measure matching. Experimental results show that IWRE can successfully solve HOIL tasks, including the challenging task of transforming the vision-based demonstrations to random access memory (RAM)-based policies under the Atari domain.Comment: 17 pages, 25 figure

The dynamics of social interaction with agents’ heterogeneity

We analyze a class of binary dynamic models inspired by [4] on agents’ choices and social interaction. The main feature of our analysis is that agents are heterogeneous, in particular their attitude to interact with the choices of the other agents changes over time endogenously. Although dynamic approaches to the study of models with heterogeneous agents have been already applied in different fields, to our knowledge a complete study of an endogenously varying population of agents has not yet been pursued. As observed in [3], the main problem is given by the fact that with heterogeneous agents the system may be non reversible. We address these problems, we describe the (possible multiple) steady states of the processes involved, we analyze local and global stability and we discuss the similarities and the differences with respect to the literature. Applications are also provided.heterogeneous agent models, intensity-based models, mean field interactions, random utilities, social interactions.

Modeling the effects of cognition on cooperation

Game theory deals with the modeling of strategic interaction between agents. Central to the predictions produced by this framework are the assumption made on rationality and how choices are made. While crucial to the outcomes predicted there exists little to no work investigating the similarities and differences between different strategy protocol updates. In this work, I develop a new protocol for simulating how agents think that I term foresight and compare and contrast it with commonly used protocols. Foresight is a way of accounting for strategic choice that is based on two well established psychological traits of humans: delayed gratification and theory of mind. It is shown that foresight is capable of overcoming the notorious second-order free-rider effect, and thereby promotes cooperation. This is shown by looking at a n-person public goods game and a simpler two-person interaction - developed specifically to mimic the former game\u27s properties.Another explanation for why agents cooperate with one another is that they do so because they are prone to making mistakes, i.e. they possess a bounded rationality. While this is a logical explanation and there exists machinery that accounts for error when predicting behavior (most notably quantal response models) there exists no mathematical framework to investigate why humans developed this bounded rationality. To address this gap in the literature I develop a model to inspect the evolution of bounded rationality. By accounting for evolutionary pressures, e.g. metabolic requirements of the brain, the possession of a bounded rationality in animals is explained