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edited by Roberto Casadei
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edited by Andrea Omicini
on 20/11/2021 00:40
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1 -1 Program
1 += 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
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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 -1.1 Schedule
12 +== Schedule ==
13 13  
14 14  10:00 - 10:05 Welcome note from the workshop chairs. Giorgio Audrito and Simon Peters
15 15  
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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.