Dynamic Coalition Formation in Iterative Request For Proposal Environments Carlos Merida-Campos Advisor: Steven Willmott Tutor: Ulises Cortés
Index 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 2
Index Document Chapter 1 1. Objective and Motivation Chapter 2 Chapter 3 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 3
Limitations on Automated Negotiation 01 Motivation & Background • Negotiations of commodities • Auction design • Bundle negotiations 4
Negotiation Between Providers 01 Motivation & Background • Reverse Auction (RFQ) • Contract Net (CNET) • Request For Proposal (RFP) 5
Limitations on RFP Research Environments 01 Motivation & Background • Limited to simple task allocation scenarios • Dynamic aspects of negotiation are usually ignored • Usually focuses on communicational aspects • Consider individual bids instead of joint proposals 6
Coalition Formation 01 Motivation & Background • Coalition Formation organizational paradigm • Solving optimization problem of each coalition • Dividing the value of the generated solution • Coalition structure generation • Dynamic Coalition formation • Assuming a series of negotiation between agents 7
Index Document Chapter 4 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 8
Theoretical Framework 02 Theoretical Framework • Aspects to consider in the model • Dynamism • Amount of information • Heterogeneity • Topology • Simultaneity 9
The General Model 02 Theoretical Framework • Tasks • Agents • Coalitions • Aggregated skills • Quantitative value • Rank • Payment 10
Agents actions 02 Theoretical Framework • Stay • Leave • Leave - Join - [replace] 11
Index Document Part II 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 12
Simple Environments 03 Model Analysis • Reduced Strategic Set • Stay • Stay if all Stay • Stay if Win • Stay if Win-2 • Leave • Random 13
Simple Environments 03 Model Analysis • System Performance in isolation 14
Simple Environments 03 Model Analysis • Individual Performance in Mixed Populations 15
Simple Environments 03 Model Analysis • Adapting using indicators • • • LMA: Local Memory Agents • GMA: Global Memory Agents 16
Simple Environments 03 Model Analysis 17
Index Document Part III 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 18
Environments With Farsighted Agents 02 Theoretical Framework • Tasks • Agents • Coalitions • Aggregated skills • Quantitative value • Rank • Payment 19
Environments With Farsighted Agents 02 Theoretical Framework • Tasks • Agents • Coalitions • Aggregated skills • Quantitative value Score Maximizing • Rank • Payment 19
Environments With Farsighted Agents 02 Theoretical Framework • Tasks • Agents • Coalitions • Aggregated skills • Quantitative value Score Maximizing • Rank • Payment Payoff Maximizing 19
Environments With Farsighted Agents 03 Model Analysis • Stability Analysis • Leading Coalition never reduces its value 20
Environments With Farsighted Agents 03 Model Analysis • Equilibrium Analysis • Optimal Leading coalition (if coalition size is not limited) 21
Environments With Farsighted Agents 03 Model Analysis • Equilibrium Analysis • Score Maximizing population converges to an equilibrium 22
Environments With Farsighted Agents 03 Model Analysis • Equilibrium Analysis • Stability is lost when requirements change Pajek Pajek Pajek 23
Environments With Farsighted Agents 03 Model Analysis • Equilibrium Analysis • Payoff maximizing systems are suboptimal and unstable Pajek 24
Environments With Farsighted Agents 03 Model Analysis • Strategies Comparison • Payoff maximizing systems are suboptimal and unstable • Correlation between performance difference and task competitiveness requirements 25
Environments With Farsighted Agents 03 Model Analysis • Strategies Comparison • Endogamic Collaboration Structures 26
Index Document Part IV 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 27
Environments With Myopic Agents 03 Model Analysis • Different Levels 28
Environments With Myopic Agents 03 Model Analysis • Different Levels 28
Environments With Myopic Agents 03 Model Analysis • Different Levels 28
Environments With Myopic Agents 03 Model Analysis • Different Levels Socially Myopic Socially Farsighted 28
Environments With Myopic Agents 03 Model Analysis • Different Levels Socially Myopic Socially Farsighted 28
Environments With Myopic Agents 03 Model Analysis • Different Levels Socially Myopic Socially Farsighted 28
Environments With Myopic Agents 03 Model Analysis • Effect of social network topologies in performance and individuals in key regions • Agent Competitiveness • Competitive • Versatile • Social Networks placement • Degree Centrality • Betweenness Centrality 29
Experiments With Myopic Agents 03 Model Analysis • Effect of social network topologies in performance and individuals in key regions 30
Environments With Myopic Agents 03 Model Analysis • Effect of social network topologies in performance and individuals in key regions • HAD Metric 31
Environments With Myopic Agents 03 Model Analysis • Effect of social network topologies in performance and individuals in key regions • Degree centrality 32
Environments With Myopic Agents 03 Model Analysis • Different Levels Farsighted Social Environments Myopic Social Environments 33
Environments With Myopic Agents 03 Model Analysis • Different Levels Farsighted Social Environments Myopic Social Environments 33
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • Which events trigger adaptation? • Which agents are reinforced? • What is the reinforcement value applied? 34
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • R - Random • K - Progressive • M - Selective • P - Selective with control 35
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • P - Selective With Control 36
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • Performance Comparison 37
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • Social Network Analysis 38
Environments With Myopic Agents 03 Model Analysis • Social Adaptation Mechanisms • Social Network Analysis Pajek 39
Index Document Part V 1. Objective and Motivation 2. Theoretical Framework 3. Results on Model Analysis • Simple Environments • Environments with Farsighted Agents • Environments with Myopic Agents • Environments with Multiple Simultaneous Tasks 4. Conclusions 40
Environments With Multiple Simultaneous Tasks 03 Model Analysis Farsighted Social Environments Myopic Static Social Environments Multiple Simmultaneous Request Environments Myopic Dynamic Social Environments 41
Environments With Multiple Simultaneous Tasks 03 Model Analysis • Intra Market Strategy • Score Maximizing • Inter Market Strategy • S - Score • R - Ranking • RSz - Ranking + Size 42
Environments With Multiple Simultaneous Tasks 03 Model Analysis • Stability Analysis • S, R. Converge • RSz. Does not necessarily converge 43
Environments With Multiple Simultaneous Tasks 03 Model Analysis • Performance Comparison Between Strategies • Variables studied • Strategies • Requests similarities • Social network density 44
Environments With Multiple Simultaneous Tasks 03 Model Analysis • Performance Comparison Between Strategies • Connection effect 45
Environments With Multiple Simultaneous Tasks 03 Model Analysis • Performance Comparison Between Strategies • Strategy effect 46
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