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AbstractIn 2014, the State Council of the Chinese Communist Party announced the institution of a social credit system by 2020, a follow-up to a similar statement on the creation of a social credit system issued by the State Council in 2007. Social credit ratings of the type being developed by the State Council in partnership with Chinese companies go beyond existing financial credit ratings in an attempt to project less-tangible personal characteristics like trustworthiness, criminal tendencies, and group loyalty onto a single scale. The emergence of personal credit ratings is enabled by Big Data, automated decision-making processes, machine learning, and facial recognition technology. It is quite likely that various kinds of personal and social credit ratings shall become reality in the near future. We explore China's version of its social credit system so far, compare the welfare and epistemological qualities of an ecology of personal ratings emanating from polycentric sourcesversusa social credit rating, and discuss whether a social credit system in an ideologically driven state is less a tool to maximize social welfare through trustworthiness provision and more a method of preventing and punishing deviance from a set of party-held ideological values.

AbstractWe develop a representation of creative evolution in economics based on the theory of the adjacent possible. We start by introducing an epistemological framework for economic theorizing that copes with unknowability and the unlistability of possibility spaces. From this framework, we discuss the use of knowledge in creatively evolving systems and derive four main results: that local knowledge is itself a mechanism of movement through the adjacent possible; that all action is entrepreneurial action; that causality is ambiguous; and that individuals can agree to disagree. We then apply these results to decision-making, innovation, and the emergence of institutions and commons in creatively evolving systems.

Dynamic stochastic general equilibrium (DSGE) modeling remains the workhorse of contemporary macroeconomics despite a growing number of critiques of its ability to explain the aggregate properties of an economic system. For the most part, those critiques accept the DSGE presumption that traditional macro data are primitive, causal data. This leads to a stipulative style of analysis where macro variables are explained in terms of one another. In contrast, we set forth an open-ended evolutionary (OEE) framework for an OEE macroeconomics. Within this framework, systems data are not primitive, but are derived from prior microlevel interactions without any presumption that those macrolevel derivations reflect systemic equilibrium among the microlevel primitive sources of action. We explore some contours of an OEE framework by placing coordination games within an ecological setting where there is no agent who has universal knowledge relevant to that ecology of games.JEL Classifications: B40, C73, D51, D85, E02