On-campus events

Join us on 27 April for a unique opportunity to sit in on a live City Speaker talk, meet the team and visit the Business School. This will be the last chance to attend an event at Cambridge Judge Business School before the final MFin application deadline.
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Ella Miron-Spektor, Associate Professor of Organisational Behaviour, INSEAD

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Fabrizio Ferraro, Professor and Head of the Strategic Management Department, IESE Business School, University of Navarra

More information on this seminar will be available nearer the time.

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Professor Glenn Carroll, Stanford Graduate School of Business

More information on this seminar will be available nearer the time.

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Arie Lewin, Professor Emeritus, Duke University

More information on this seminar will be available nearer the time.

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Marya Besharov, Professor of Organisations and Impact, Saïd Business School, University of Oxford

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Professor Agni Orfanoudaki, Saïd Business School of Oxford University

There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making is not well understood. Our work attempts to address this problem, investigating the effect of algorithmic predictions on human expert judgment. Leveraging an online survey of medical providers and data from a leading US hospital, we develop an ML algorithm and compare its performance with that of medical experts in the task of predicting 30-day readmissions after solid-organ transplantation. We find that our algorithm is not only more accurate in predicting clinical risk but can also positively influence human judgment. However, its potential impact is mediated by the users’ degree of algorithm aversion and trust. We show that, while our ML algorithm establishes non-linear associations between patient characteristics and the outcome of interest, human experts mostly attribute risk in a linear fashion. To capture potential synergies between human experts and the algorithm, we propose a human-algorithm “centaur” model. We show that it can outperform human experts and the best ML algorithm by systematically enhancing algorithmic performance with human-based intuition. Our results suggest that implementing the centaur model could reduce the average patient readmission rate by 26.4%, yielding up to a $770,000 reduction in annual expenditure at our partner hospital and up to $67 million savings in overall U.S. healthcare expenditures.
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Professor Gerben van der Vegt, University of Gronigen

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Professor Patrick Haack, HEC Lausanne

We advance research on legitimacy by theorising a multilevel model of how an individual legitimacy belief—propriety—is affected by an exogenous shock and how two collective constructs—validity and consensus—influence this effect. Whereas validity can reflect underlying consensus, the constructs are not the same, given that validity may hide disagreement and thus low consensus. Disentangling validity from consensus allows us to theorise that changes in evaluators’ propriety beliefs are strongest following exogenous shocks characterised by high validity and low consensus, because a shock may reveal that validity was contested. Using a regression discontinuity design based on data from 6,198 evaluators across 16 countries, we examine the effect of the 2008 financial crisis on individuals’ propriety beliefs in free markets. Our results provide empirical support for our theory and thereby shed light on how legitimacy changes in the wake of an exogenous shock.

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