How Intermittent Breaks in Interaction Improve Collective Intelligence

Ethan  Bernstein; Jesse  Shore; David  Lazer

Article | Proceedings of the National Academy of Sciences of the United States of America | August 28, 2018

How Intermittent Breaks in Interaction Improve Collective Intelligence

by Ethan Bernstein, Jesse Shore and David Lazer

Abstract

People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects’ best solutions so that they could be reloaded and possibly modified in subsequent rounds—a ubiquitous feature of personal productivity software—is similar to constant social influence: it increases mean performance but decreases exploration.

Keywords: transparency; social influence; Collective Intelligence; Interaction; problem solving; collaboration; intermittant; Breaks; always on; communication technologies; communication; Communication; Design; Information; Management; Leadership; Organizational Design; Organizational Structure; Performance; Social and Collaborative Networks; Information Technology;

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Citation:

Bernstein, Ethan, Jesse Shore, and David Lazer. "How Intermittent Breaks in Interaction Improve Collective Intelligence." Proceedings of the National Academy of Sciences of the United States of America 115, no. 35 (August 28, 2018).

Each individual who enters an organization will, at some point, leave. And yet most future leaders spend significantly more effort learning about recruiting than departures, despite the sensitivity and challenges associated with the latter. This note is intended to help address that imbalance. This note provides a roadmap for managers and employees to follow when instituting or facing layoffs. It aims to help managers consider the broader implications that should be taken into account when conducting layoffs, including how to structure and execute them effectively; how to minimize the negative impact on laid-off employees, “surviving” employees, the firm’s reputation, and the community; and what alternatives any firm contemplating layoffs should consider.

From the outside, DISCO—a Japan-based manufacturer of precision tools for semiconductor production devices—appeared to be a rather ordinary company that had achieved rather extraordinary success: it had simultaneously achieved 70% global market share, had lifted its profitability from 15% to 30% over the past seven years, and was consistently selected as a "Best Workplace" in Japan. The secret to DISCO’s success, according to CEO Kazuma Sekiya, lies in its truly individualized management of human capital, which they called the P-Will (Personal Will) system.
P-Will was a proprietary managerial accounting system based on Will, a currency that enabled internal market transactions. At DISCO, every hour of labor and every good was associated with a price in Will. Employees were expected to act like independent business owners; they used the P-Will system to manage their own revenue contributions and business expenses in Will. Four times a year, the balances of individual P-Will accounts were converted to real currency and paid out as bonuses.
In the last couple of years, about 10 companies, including other companies in the semiconductor industry, had visited Sekiya wanting to replicate DISCO’s enviable success and introduce the P-Will system at their companies. However, none of these companies had actually implemented it. Seeing off another guest who had visited DISCO, Sekiya asked himself: Why don’t other companies adopt the P-Will system? What were the conditions under which P-Will would work—and not work?

Case | HBS Case Collection | October 2018

Safecast: Bootstrapping Human Capital to Big Data

Ethan Bernstein and Stephanie Marton

On March 11, 2011 at 2:46pm, a 9.1-on-the-Richter-scale, six-minute long earthquake unleashed a tsunami that ravaged the Tohoku region of Japan, damaging the Fukushima Daiichi Nuclear Power facility and releasing sufficient radioactive material into the air and ocean to make it one of only two “level 7” nuclear disasters in history (second only to Chernobyl).
But just how much radioactive material had escaped was not clear. A tense time was made worse by sporadic disclosures of fragmented information. Those who had power or cell service, mostly friends and family outside the region, were glued to their television and smartphone screens, but, by definition, no one could see the radiation they feared.
Frustrated by their own desires to know what should have been knowable, three technologists—Sean Bonner, Pieter Franken and Joi Ito—founded non-profit Safecast around a volunteer-centered, open, citizen-science, “crowdmapping” model to monitor radiation levels. With the help of thousands of volunteers, by 2018, Safecast had become not just the “go to” source of information on radiation issues in Japan and elsewhere, but also the vanguard example of citizen science.
Yet as Safecast’s dataset strengthened exponentially, the sustainability of its financial model weakened. The same open, crowd-based model that made the founder’s data collection sustainable was still financially unsustainable. Was there a business model that could sustain the organization financially without undermining their volunteer-based operations, and if so, what would it look like?

Keywords: citizen science; creative commons; Open Data; open architecture; volunteer-based organization; Fukushima Daiichi Nuclear Power Facility; 311; nuclear; radiation; crowdsourcing; bgeigie; geiger counters; kickstarter; sustainability; sustainable business and innovation; Design; Energy Generation; Social Entrepreneurship; Human Capital; Innovation and Invention; Crisis Management; Organizational Structure; Organizational Design; Technology; Business Model; Energy Industry; Technology Industry; Japan; North and Central America; Europe;

Citation:

Bernstein, Ethan, and Stephanie Marton. "Safecast: Bootstrapping Human Capital to Big Data." Harvard Business School Case 419-033, October 2018. View Details

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