In the first comprehensive and full-length study of the English historical economists, Gerard Koot traces their revolt against the theory, policy recommendations and academic dominance of classical and neoclassical economics in Britain between 1870 and 1926. English Historical Economics, 1870–1926 shows how these historical critics challenged the deductive method and mechanistic assumptions of the economic orthodoxy, developing an historical and inductive method for economic studies and laying the foundation for the professional study of economic history. The author examines the (...) effect of this new methodology upon English politics, discussing the intellectual framework that the historical economists provided for the conservative attack on laissez-faire philosophy in links between such larger social, economic, political and intellectual controversies and the origin and growth of English historical economics. (shrink)

There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and (...) society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool—data selection and pre-processing, training of the algorithm and post-processing and adoption—inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps. (shrink)