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Collaboration among computational agents through indirect reciprocity

Vie, Simen Tjøtta
Master thesis
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URI
http://hdl.handle.net/11250/2615801
Date
2018
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  • Institutt for datateknologi og informatikk [7357]
Abstract
Indirect reciprocity is a mechanism for collaboration from evolutionary theory that attempts

to explain the occurrence of altruism. It explains how a population of entities can

make a decision of whether to cooperate in an interaction on the basis of the reputation

of others. This basis of collaboration is largely seen in humans. We possess superior

communicative and cognitive abilities compared to other species, enabling us to share and

evaluate information through rumors of what happens between other people. There are

limitation in the effectiveness in the indirect reciprocity we see in humans. People only

collaborate when they have a reason to do so, and will hold back if there is uncertainty

if an act of helping will ever be reciprocated. The lack of trust and uncertainty increases

with the number of people that partake in such a mechanism, as we have a hard time

communicating and reasoning about large amounts of information.

We identify that uncertainty in the evaluation of other peoples reputation, as well as the

cost of maintaining a reputation on an entire population acts as limitations of indirect

reciprocity.

Representing humans through computational agents allows us to increase the mere size

of information we are able to communicate and reason about. We provide agents with a

public observation model using blockchain technology and smart contracts, enabling them

to have access to information about encounters between the other agents. The agents are

granted with certainty that the information from the observation model is correct. They

sign contracts as proof that interactions occur using asymmetric encryption. The resulting

contracts are used as a basis for the evaluation of reputation of other agents. We use social

norms to define good behaviour, and consequently how the agents assess the reputation

of other agents. Every agent conforms to a social norm of their choice, and as a consequence

they have a subjective opinion on which agents have good reputation. Through

social learning by imitating agents that perform better, the population converges to a homogeneous

one in terms of social norms. Using a strict discriminating social norm such

as stern-judging, we see a near perfect cooperation rate where the population successfully

discriminates agents employing other social norms.
Publisher
NTNU

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