The ability to sustain indirect reciprocity is an example of collective intelligence. It is increasingly relevant to future technology and autonomous machines that need to function in a coalition. Indirect reciprocity involves providing benefit to others without guaranteeing a future return. The identity through which an agent presents itself to others is fundamental, as this is how the reputation of an agent is considered. In this paper, we examine the sharing of identity between agents, which is an important and frequently overlooked issue when considering indirect reciprocity. We model an agent's identity using traits, which can be shared with other agents, and offer a basis for an agent to change their identity. Through this approach, we determine how shared identity affects cooperation, and the conditions through which cooperation can be sustained. This also helps us to understand how and why behavioural strategies involving identity function are put in place, such as whitewashing. The framework offers the opportunity to assess the interplay between the sharing of traits and the cost, in terms of reduced cooperation and opportunities for shirkers to benefit.
Cooperation is a sophisticated example of collective intelligence. This is particularly the case for indirect reciprocity, where benefit is provided to others without a guarantee of a future return. This is becoming increasingly relevant to future technology, where autonomous machines face cooperative dilemmas. This paper addresses the problem of stereotyping, where traits belonging to an individual are used as proxy when assessing their reputation. This is a cognitive heuristic that humans frequently use to avoid deliberation, but can lead to negative societal implications such as discrimination. It is feasible that machines could be equally susceptible. Our contribution concerns a new and general framework to examine how stereotyping affects the reputation of agents engaging in indirect reciprocity. The framework is flexible and focuses on how reputations are shared. This offers the opportunity to assess the interplay between the sharing of traits and the cost, in terms of reduced cooperation, through opportunities for shirkers to benefit. This is demonstrated using a number of key scenarios. In particular, the results show that cooperation is sensitive to the structure of reputation sharing between individuals.
With the rising number of people using social networks after the pandemic of COVID-19, cybercriminals took the advantage of (i) the increased base of possible victims and (ii) the use of a trending topic as the pandemic COVID-19 to lure victims and attract their attention and put malicious content to infect the most possible number of people. Twitter platform forces an auto-shortening to any included URL within a 140-character message called “tweet” and this makes it easier for the attackers to include malicious URLs within Tweets. Here comes the need to adopt new approaches to resolve the problem or at least identify it to better understand it to find a suitable solution. One of the proven effective approaches is the adaption of machine learning (ML) concepts and applying different algorithms to detect, identify, and even block the propagation of malware. Hence, this study’s main objectives were to collect tweets from Twitter that are related to the topic of COVID-19 and extract features from these tweets and import them as independent variables for the machine learning models to be developed later, so they would identify imported tweets as to be malicious or not.
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