We propose a measure of technological progress based on the information embedded in standard input-output tables. A connection is established between the quantities necessary as inputs, the associated output and auxiliary prices. It is argued that the wage-profit frontiers and the associated production prices together provide a robust basis for measuring technological progress and productivities. The computation of the wage-profit frontiers is a non-trivial exercise because of high combinatorial complexity. An algorithm that renders this computation feasible is presented. We analyze technological progress and productivities among 30 countries between 1995-2011 using the latest multi-regional input-output data.

This paper examines caste-based differences in farmers' access to bank loans in rural India. We investigate whether banks practice taste-based discrimination on the basis of caste. In order to identify potential discrimination, we consider loan applications and approval decisions separately. We find significant inter-caste differences in application rates, and evidence of discrimination against Scheduled Tribe borrowers at the approval stage. To rule out the role of statistical discrimination, we simulate unobserved credit histories with various distributions. Evidence for taste-based discrimination persists despite accounting for unobservables. However, we find that this discrimination does not affect small farmers.

In this article we propose a process-based definition of big data, as opposed to the sizeand technology-based definitions. We argue that big data should be perceived as a continuous, unstructured and unprocessed dynamics of primitives, rather than as points (snapshots) or summaries (aggregates) of an underlying phenomenon. Given this, we show that big data can be generated through agent-based models but not by equation-based models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agent-based models can aid in evaluating the quality (interpreted as information aggregation efficiency) of big data. Based on this, we argue that agent-based modelling can serve as a possible foundation for big data. We substantiate this interpretation through some pioneering studies from the 1980s on swarm intelligence and several prototypical agent-based models developed around the 2000s.

In this paper, we consider learning by human beings and machines in the light of Herbert Simon's pioneering contributions to the theory of Human Problem Solving. Using board games of perfect information as a paradigm, we explore differences in human and machine learning in complex strategic environments. In doing so, we contrast theories of learning in classical game theory with computational game theory proposed by Simon. Among theories that invoke computation, we make a further distinction between computable and computational or machine learning theories. We argue that the modern machine learning algorithms, although impressive in terms of their performance, do not necessarily shed enough light on human learning. Instead, they seem to take us further away from Simon's lifelong quest to understand the mechanics of actual human behaviour. Keywords Machine learning • Human problem solving • Herbert Simon • Learning • Artificial intelligence • Go We thank Prof. Vela Velupillai for introducing us to the works of Simon and his imaginative interpretations of Simon's contributions. We have benefited immensely over the years from his writings and many discussions we had on these topics. We are grateful to Diviya Pant, Ierene Francis, Jessica Paul, Kavikumar and Sarath Jakka for their comments and suggestions on earlier drafts. The errors in interpretation are, alas, our own.

AUTHORPrevious attempts to understand the functioning of cooperative banks have often considered them as being similar to credit unions. However, we argue that credit unions are only a subset of cooperative financial institutions and the models used to describe their behavior cannot be generalized to all cooperative banks. Additionally, there is an important factor that characterizes cooperative banks' behavior and outcomes, which does not apply to credit unions: the role of nonmembers and their contribution to the members' overall welfare through bank deposits and interest earnings. In this paper, we move from the Smith et al. (1981) model developed to describe credit unions' pricing policy on interest rates and we propose a more general model by incorporating nonmember depositors and borrowers, who play a key role in determining cooperative banks' interest rates.