Endogenous Firm Dynamics and Labor Flows via Heterogeneous Agents ✶ ✶Support from the John D. and Catherine T. MacArthur Foundation, the National Science Foundation (0738606), the Small Business Administration (SBAHQ-05-Q-0018), and the Mercatus Center at George Mason is gratefully acknowledged. I have no relevant or material financial interests that relate to the research described in this paper or the associated model. Earlier versions of this work were presented at research institutions (Aix-en-Provence

Axtell, Robert L.

doi:10.1016/bs.hescom.2018.05.001

Cited by 8 publications

(2 citation statements)

References 94 publications

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“…Data-driven agent computing combined with machine learning can be used to model the world from the bottom up, since the individual components of economic or social systems (such as workers or firms) all generate large amounts of specific data, which allows computational social scientists to generate models of whole economies (Axtell, 2018) and epidemiologists to expand the modelling of pandemics and other problem waves. In the public sector, agent computing is especially valuable in policy simulations, allowing policy makers to test out interventions without experiencing unintended consequences or undertaking emergency planning.…”

Section: Data-intensive Information Regimes and Digital Decompressionmentioning

confidence: 99%

“…Large-scale data and data science methodologies such as agent-based computing allow the possibility of 'policy holism', where data-driven approaches can feed directly into policy design. For example, simulations (such as that of whole economies, see Axtell, 2018) offer new possibilities for modelling policy interventions before putting them into practice Dorobantu, 2019, 2022). Alternatively, interventions might draw in data from across multiple 'intelligent centres' in different sectors or exploit wider societal-level data (e.g., labour market data) to focus on particular policy problems.…”

Section: Administrative Holismmentioning

confidence: 99%

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Data science, artificial intelligence and the third wave of digital era governance

Dunleavy,

Margetts

2023

Preprint

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This article examines the model of digital era governance (DEG) in the light of the latest-wave of data-driven technologies, such as data science methodologies and artificial intelligence (labelled here DSAI). It identifies four key top-level macro-themes through which digital changes in response to these developments may be investigated. First, the capability to store and analyse large quantities of digital data obviates the need for data 'compression' that characterises Weberian-model bureaucracies, and facilitates data de-compression in data-intensive, information regimes, where the capabilities of public agencies and civil society are both enhanced. Second, the increasing capability of robotic devices have expanded the range of tasks that machines extending or substituting workers' capabilities can perform, with implications for a reshaping of state organisation. Third, DSAI technologies allow new ways of partitioning state functions in ways that can maximise organisational productivity, in an 'intelligent centre, devolved delivery' model within vertical policy sectors. Fourth, within each tier of government, DSAI technologies offer new possibilities for 'administrative holism' - the horizontal allocation of power and functions between organisations, through state integration, common capacity and needs-based joining-up of services. Together, these four themes comprise a third wave of DEG changes, suggesting important administrative choices to be made regarding information regimes, state organisation, functional allocation and outsourcing arrangements, as well as a long-term research agenda for public administration, requiring extensive and detailed analysis. This article has been accepted for publication in the Sage journal Public Policy and Administration, August 2023.

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Section: Data-intensive Information Regimes and Digital Decompressionmentioning

confidence: 99%

Section: Administrative Holismmentioning

confidence: 99%

Data science, artificial intelligence and the third wave of digital era governance

Dunleavy,

Margetts

2023

Preprint

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Computational Social Science for Public Policy

Margetts

Dorobantu

2023

Handbook of Computational Social Science for Policy

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Computational Social Science (CSS), which brings together the power of computational methods and the analytical rigour of the social sciences, has the potential to revolutionise policymaking. This growing field of research can help governments take advantage of large-scale data on human behaviour and provide policymakers with insights into where policy interventions are needed, which interventions are most likely to be effective, and how to avoid unintended consequences. In this chapter, we show how Computational Social Science can improve policymaking by detecting, measuring, predicting, explaining, and simulating human behaviour. We argue that the improvements that CSS can bring to government are conditional on making ethical considerations an integral part of the process of scientific discovery. CSS has an opportunity to reveal bias and inequalities in public administration and a responsibility to tackle them by taking advantage of research advancements in ethics and responsible innovation. Finally, we identify the primary factors that prevented Computational Social Science from realising its full potential during the Covid-19 pandemic and posit that overcoming challenges linked to limited data flows, siloed models, and rigid organisational structures within government can usher in a new era of policymaking.

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Coping with high decline: firms’ resilience to adversity

2023

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This work investigates the factors that precipitate a firm’s sudden high decline, which is defined as a short-term heavy contraction in firm size, and examines firms’ performance in the aftermath of a high-decline (HD) event. The empirical analysis reveals patterns of HD events over the business cycle and across markets, providing insights into the factors that enable firms’ resilience in terms of better growth performance after an HD event. Firms that upgrade their production processes and invest in enhancing their human capital show better growth trajectories in the aftermath of an HD event.

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Cited by 8 publications

References 94 publications

Data science, artificial intelligence and the third wave of digital era governance

Data science, artificial intelligence and the third wave of digital era governance

Computational Social Science for Public Policy

Coping with high decline: firms’ resilience to adversity

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