Led by Professor Debin Ma, the State Capacity, Institutions, and Development Research Cluster quantifies state capacity in historical China, examining its evolution, regional variations, and impact on social and economic outcomes.
State capacity describes the ability of a state to collect taxes, enforce law and order, secure property rights and provide other public goods (Besley & Persson, 2011). Over the last few centuries, the world has witnessed an unprecedented increase in wealth as well as a remarkable transformation in the scope and scale of the state. The richest countries are characterised by long-lasting, centralised political institutions, whereas poverty is widespread in countries that are internally fragmented and lack a history of centralised governance. It is important to quantify state capacity in historical China, understand how it evolved over time and differed across regions and investigate how state capacity affected social and economic outcomes. The State Capacity, Institutions and Development Research Cluster investigates these and other questions quantitatively, as this is one of the most important pieces of the China puzzle.
In process mining, extensive data about an organizational process is summarized by a formal mathematical model with well-grounded semantics. In recent years a number of successful algorithms have been developed that output Petri nets, and other related formalisms, from input event logs, as a way of describing process control flows. Such formalisms are inherently constrained when reasoning about the probabilities of the underlying organizational process, as they do not explicitly model probability. Accordingly, this paper introduces a framework for automatically discovering stochastic process models, in the form of Generalized Stochastic Petri Nets. We instantiate this Toothpaste Miner framework and introduce polynomial-time batch and incremental algorithms based on reduction rules. These algorithms do not depend on a preceding control-flow model. We show the algorithms terminate and maintain a deterministic model once found. An implementation and evaluation also demonstrate feasibility.
