Changes for page Program
From version 3.1
edited by Roberto Casadei
on 02/03/2021 08:54
on 02/03/2021 08:54
Change comment:
Rollback to version 1.1
To version 4.1
edited by Andrea Omicini
on 20/11/2021 00:40
on 20/11/2021 00:40
Change comment:
Document converted from syntax xwiki/1.0 to syntax xwiki/2.1
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... ... @@ -1,8 +1,8 @@ 1 - 1Program1 += Program = 2 2 3 +**When**: Friday, August 21th, 2020 (online) 3 3 4 -*When*: Friday, August 21th, 2020 (online) 5 -1.1 Accepted full papers 5 +== Accepted full papers == 6 6 7 7 * Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou, Johannes Grohmann, Ingrid Nunes, Sven Tomforde and Evangelos Pournaras: Data-driven Analysis for Design Patterns in Collective Self-adaptive Systems 8 8 * Hunza Zainab, Giorgio Audrito, Soura Dasgupta and Jacob Beal: Improving Collection Dynamics by Monotonic Filtering ... ... @@ -9,7 +9,7 @@ 9 9 * Nicolás Cardozo and Ivana Dusparic: Language Abstractions and Techniques for Developing Collective Adaptive Systems Using Context-oriented Programming 10 10 * Roberto Casadei, Mirko Viroli and Alessandro Ricci: Collective Adaptive Systems as Coordination Media: The Case of Tuples in Space-Time 11 11 12 - 1.1Schedule12 +== Schedule == 13 13 14 14 10:00 - 10:05 Welcome note from the workshop chairs. Giorgio Audrito and Simon Peters 15 15 ... ... @@ -39,24 +39,6 @@ 39 39 40 40 15:50 - 16:00 Workshop closing 41 41 42 +== Keynote Abstract == 42 42 43 -1.1 Keynote Abstract 44 - 45 -In open complex adaptive systems, the components may be owned by 46 -different individuals who can have conflicting interests. A key 47 -challenge is then to produce a system that performs well at the global 48 -level, even when the components are each optimising their own behaviour 49 -to try to satisfy their own preferences with no regard for the global 50 -outcome. Using three examples, I will demonstrate how an evolutionary 51 -game theory analysis can inform the construction of systems that meet 52 -this goal. In the first example, I will introduce evolutionary game 53 -theory and show how it can help us to design institutions that prevent 54 -overexploitation of common pool resources that are shared by many 55 -agents. In the second example, I will illustrate how evolutionary game 56 -theory can guide the development of a mechanism for reducing the peak 57 -electricity consumption of a group of households in a smart grid. 58 -Finally, I will turn to discuss issues of trust in interactions between 59 -people and intelligent agents, and show how an evolutionary game theory 60 -model can be used to make testable predictions about when people will or 61 -will not trust intelligent agents. 62 - 44 +In open complex adaptive systems, the components may be owned by different individuals who can have conflicting interests. A key challenge is then to produce a system that performs well at the global level, even when the components are each optimising their own behaviour to try to satisfy their own preferences with no regard for the global outcome. Using three examples, I will demonstrate how an evolutionary game theory analysis can inform the construction of systems that meet this goal. In the first example, I will introduce evolutionary game theory and show how it can help us to design institutions that prevent overexploitation of common pool resources that are shared by many agents. In the second example, I will illustrate how evolutionary game theory can guide the development of a mechanism for reducing the peak electricity consumption of a group of households in a smart grid. Finally, I will turn to discuss issues of trust in interactions between people and intelligent agents, and show how an evolutionary game theory model can be used to make testable predictions about when people will or will not trust intelligent agents.