Opening up the black box: Technological transparency and prevention

• Published date: 01 September 2021
• Author: Lu Li
• Date: 01 September 2021
• DOI: http://doi.org/10.1111/jori.12328

J Risk Insur. 2021;88:665–693. wileyonlinelibrary.com/journal/JORI

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665

Received: 22 January 2020

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Accepted: 23 August 2020

DOI: 10.1111/jori.12328

ORIGINAL ARTICLE

Opening up the black box: Technological

transparency and prevention

Lu Li

Institute for Risk Management and

Insurance, Munich School of Management,

Ludwig‐Maximilians‐University (LMU)

Munich, Munich, Germany

Correspondence

Lu Li, Institute for Risk Management and

Insurance, Munich School of Management,

Ludwig‐Maximilians‐University (LMU)

Munich, Munich 80539, Germany.

Email: li@bwl.lmu.de

Abstract

We discuss the behavioral and welfare implications of

uncovering determinants of successful prevention. Based

on a novel reinterpretation of prevention, we introduce

the concept of technological transparency (TT)—the

extent to which scientific knowledge allows agents to

predict the success of their effort conditional on ob-

servable risk determinants. When risk determinants are

observable ex ante, TT refines the information partition

and induces more efficient prevention but does not ne-

cessarily improve welfare when the risk is insurable. At

thesametime,TTmayharmwelfare if information is

incompletely disclosed. When risk determinants are only

observable ex post, TT may increase effort by triggering

future regret. Our framework facilitates a deeper un-

derstanding of the connection between knowledge and

theefficientchoiceofpreventiveeffort.Ourfindings

inform the cost‐benefit analysis of advancing knowledge

about risk processes, as well as the effective disclosure of

such knowledge to the public.

KEYWORDS

prevention, regret, technological transparency,

value of information

JEL CLASSIFICATION

D61; D80; D90; H00

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

© 2020 The Authors. Journal of Risk and Insurance published by Wiley Periodicals LLC on behalf of American Risk and Insurance

Association

1|INTRODUCTION

“Success = talent + luck. Great success = a little more talent + a lot of luck”

Daniel Kahneman, Thinking, Fast and Slow

Most life outcomes depend inevitably on both our own actions and factors beyond our

control. Disentangling the roles of luck and effort, however, is often not trivial. In any situation

where the efficacy of effort is interpreted in terms of the probability of some event, the exo-

genous determinants of that event are completely hidden. A prominent example of such is self‐

protection (also referred to as loss prevention, see Courbage, Rey, & Treich, 2013; Ehrlich &

Becker, 1972), which is a costly effort to reduce the likelihood of a loss event. For instance,

while healthy diet and regular physical exercise help reduce the probability of developing

diabetes, the successful prevention of diabetes is shown to also depend on exogenous factors

including one's genetic makeup (Frayling, 2007). However, which exact genes are involved in

this process, as well as the complex mechanism of this gene–lifestyle interaction, are still far

from being perfectly understood (L. Qi, Hu, & Hu, 2008).

As in the example mentioned above, any self‐protection technology has an inherent pos-

sibility of failing. While an agent knows by how much a larger effort is more likely to succeed,

she does not know the risk determinants, that is, factors that determine the actual success of her

effort. Without knowledge about the risk determinants, any self‐protection technology re-

sembles a black box as the mechanism of its success is invisible to the agent. How can we shed

light into this black box and what happens if we do?

We propose the concept of technological transparency (TT), which describes the extent to

which scientific knowledge allows agents to predict the success of prevention conditional on

observable risk determinants. The more risk determinants are uncovered by scientific research,

the better we are able to explain and predict the success of any effort. In its extreme form, full

TT reveals all risk determinants so that an agent can perfectly predict whether or not an effort

will succeed as soon as she observes those risk determinants. An improvement of TT refers to

the process of uncovering previously unknown risk determinants so that the success of effort

can be predicted with higher precision.

One simple example of TT can be seen from the history of blood transfusion. It is common

knowledge today that the success of blood transfusion is predominately determined by people's

blood type. For simplicity, assume blood type is the only determinant of successful blood

transfusion. Before different blood types were discovered in 1901 (Landsteiner, 1900), blood

transfusion had been seen as a highly risky activity that occasionally succeeded but often

failed.

1

Discovering blood types and understanding their role in determining successful blood

transfusion is an example of obtaining full TT. Through revealing the mechanism of successful

blood transfusion, full TT transformed blood transfusion from a highly unreliable treatment

method to one whose success can be perfectly predicted conditional on knowing the blood types

of the donor and the recipient.

Consider another example where the effort can take multiple values, say an investment to

reinforce a house to prevent it from being destroyed by a hurricane. The more investment is

made, the more likely the house will be successfully protected, but the actual success still

depends on the intensity of the hurricane. Without TT, all we know is how the loss probability

1

The first successful blood transfusion documented in human history was performed in 1667, according to https://www.

heart-valve-surgery.com/heart-surgery-blog/2009/01/03/first-blood-transfusion/.

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