What makes Individual I's a Collective We; Coordination mechanisms & costs

For a collective to become greater than the sum of its parts, individuals' efforts and activities must be coordinated or regulated. Not readily observable and measurable, this particular aspect often goes unnoticed and understudied in complex systems. Diving into the Wikipedia ecosystem, where people are free to join and voluntarily edit individual pages with no firm rules, we identified and quantified three fundamental coordination mechanisms and found they scale with an influx of contributors in a remarkably systemic way over three order of magnitudes. Firstly, we have found a super-linear growth in mutual adjustments (scaling exponent: 1.3), manifested through extensive discussions and activity reversals. Secondly, the increase in direct supervision (scaling exponent: 0.9), as represented by the administrators' activities, is disproportionately limited. Finally, the rate of rule enforcement exhibits the slowest escalation (scaling exponent 0.7), reflected by automated bots. The observed scaling exponents are notably robust across topical categories with minor variations attributed to the topic complication. Our findings suggest that as more people contribute to a project, a self-regulating ecosystem incurs faster mutual adjustments than direct supervision and rule enforcement. These findings have practical implications for online collaborative communities aiming to enhance their coordination efficiency. These results also have implications for how we understand human organizations in general.Comment: 27 pages, 7 figure

Prediction and Optimal Scheduling of Advertisements in Linear Television

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of impressions in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue

Prediction and Optimal Scheduling of Advertisements in Linear Television

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of impressions in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue

Collective Intelligence as Infrastructure for Reducing Broad Global Catastrophic Risks

Academic and philanthropic communities have grown increasingly concerned with global catastrophic risks (GCRs), including artificial intelligence safety, pandemics, biosecurity, and nuclear war. Outcomes of many risk situations hinge on the performance of human groups, such as whether democratic governments and scientific communities can work effectively. We propose to think about these issues as Collective Intelligence (CI) problems -- of how to process distributed information effectively. CI is a transdisciplinary perspective, whose application involves humans and animal groups, markets, robotic swarms, collections of neurons, and other distributed systems. In this article, we argue that improving CI can improve general resilience against a wide variety of risks. Given the priority of GCR mitigation, CI research can benefit from developing concrete, practical applications to global risks. GCR researchers can benefit from engaging more with behavioral sciences. Behavioral researchers can benefit from recognizing an opportunity to impact critical social issues by engaging with these transdisciplinary efforts.Comment: 9 pages, 3 figures. Perspective articl