Indirect Reciprocity with Private Assessments - Problems and Solutions

  • Marcus Krellner

Student thesis: Doctoral Thesis


You shall treat others as you want to be treated! This rule is widely endorsed
throughout many major religions. It is at the core of peaceful and fruitful
cooperation. And it reflects one of the major contributions of mathematics
to our understanding of human morality: reciprocity.
The idea of reciprocity is that you are literally treated as you had treated
others before. Be it in a back and forth with a specific individual - direct
reciprocity - or how you treat others in general - indirect reciprocity (IR). Such
IR might be the ultimate reason why we care who did what to whom, why
we gossip about others and guard our own reputation. We would like that
others get what they deserve (the nice and the bad), but even more we do
want to be treated nicely ourselves. How can we reciprocate that someone was
unkind, without appearing unkind ourselves?
For a time it seemed that researchers had discovered a winning principle,
namely justified punishment. We implement the rule, that your reputation
is decreased, when you are unkind to a good person, but not when you are
unkind to a bad person. However, who decides who is a bad person? Do
we not sometimes differ in our judgements of others? Indeed, when we all
observe and judge each other independently, so-called private assessments
(priA), IR is disturbed.
In this thesis, we provide four contributions to understand and solve the
problems of private assessments. First, IR cannot be improved by having
more different or even more specific rules than justified punishment. Second,
when we please others, by acting according to their opinions instead of our
own, IR can be stabilised. Third, IR performs well under private assessments
when we implement a system of joint commitment, so people are only judged if they had made a promise to cooperate. And finally, we develop the first
analytical model of disagreements under priA. It can be used to predict
the behaviour of classic IR strategies, but also of new approaches such as
Date of Award16 Feb 2024
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorYifeng Zeng (Supervisor), The Anh Han (Supervisor) & Simon Lynch (Supervisor)

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