Une approche multi-agent basée sur la confiance pour évaluer la performance des plateformes de crowdsourcing d idées

Transcription

1 Laboratoire en Innovation, Technologie, Économie et Management Une approche multi-agent basée sur la confiance pour évaluer la performance des plateformes de crowdsourcing d idées Amine Louati Christine Balagué Mehdi Elmoukhliss Institut mines Télécom, Télécom École de Management, LITEM, France Journées Francophones sur les Systèmes Multi-Agents (JFSMA) Jeudi 06 Juillet 2017 A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 1 / 28

2 Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 2 / 28

3 Context Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 3 / 28

4 Context Context: Crowd Innovation A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 4 / 28

5 Context Existing research Model users and their relationships using a coopetition network [Hu et Zhang,2014] [Levine and Prietula, 2014] Focus on examining users behavior (e.g., roles, motivation,... ) [Bullinger et al.,2010] [Muhdi and Boutellier, 2011] Conduct exploratory analysis on static data derived using quantitative and qualitative methods [Hutter et al.,2011] [Fuller et al. 2014] To improve idea generation quality Limits Mono-relational model of the coopetition network Lack support for the social dimension (trust, interactional history,... ) Little is known about the social dynamics of users and how their relationships may evolve in the platform A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 5 / 28

6 Context Our solution To address these three limits, we propose: A multi-relational coopetition network model which takes into consideration cooperative, competitive and coopetitive relationships. A trust model as trust is considered as the main mechanism that enables agents to reason about the confidence of others and guides their decision-making process when they want to interact. An agent-based simulation as agents have demonstrated the ability to support interactions and their dynamics while using reasoning, extraction and representation of knowledge as well as social metaphors like trust. A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 6 / 28

7 Concepts and Agent Architecture Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 7 / 28

8 Concepts and Agent Architecture Concepts definition Snapshot G i =< V, E i, t i > V = {a 1, a 2,..., a n } is a set of n agents; E i = {E i,pos, E i,neg } is a set of directed edges where E i,l V V l {pos, neg} is the set of edges w.r.t the interaction type; t i is a time period. Coopetition network G [t0,t z ] =< V, E, W > V = {a 1, a 2,..., a n } is a set of n agents; E = {E coo, E com, E cop } is a set of directed edges where E l V V l {coo, com, cop} is the set of edges w.r.t the R l relationship; W : E [0, 1] is a weight function mapping directed edges to their trust values; A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 8 / 28

9 Concepts and Agent Architecture Agent architecture Selon Castelfranchi et al., 1998 : Only a cognitive agent can trust another agent. a k i Ro k Pa k PIT k Figure: Architecture of a cognitive agent based on trust A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 9 / 28

10 Trust model Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 10 / 28

11 Trust model Trust model Our trust model is composed of two dimensions: The environmental dimension: corresponds to the perception evaluated on the basis of information extracted from the coopetition network Climatic trust Social trust The dyadic dimension: represents the confidence of one in another and computed on the basis of their interactional history while taking into account the time-related aspect in the building process. A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 11 / 28

12 Trust model Climatic Trust Observed Trend (Tr i ) Let G i 1 =< V, E i 1, t i 1 > be a snapshot of time period t i 1 Pset i be the set of perceived actions randomly chosen from E i 1 Tr i =< V, E >, is a sub-graph of G i 1 induced by Pset such as V V is a set of agents and E = E i 1 V is the set of directed edges between them CTrust(a k, Tr i ) = cardinal({ac+ Pset i }) cardinal(pset i ) A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 12 / 28

13 Trust model Social Trust Egocentric Network Let G [t0,t i ] be a coopetition network graph Z N be the relative strength of memory H [ti Z,t i ] =< V, E > is a sub-graph of G [ti Z,t i ] centered on a k such as i V = {a j V (a k, a j ) G [ti Z,t i ]} {a k } and E ={ E x a k } x=i S STrust(a k, H [ti Z,t i ]) = cardinal({ac+ E }) cardinal(e ) A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 13 / 28

14 Trust model Environmental Dimension Trust aggregation ETrust(a k, Tr i H [ti Z,t i ])) = w CTrust(Tr i )+(1 w) STrust(a k, H [ti S,t i ])) The environmental trust value is used to qualify the perception ETrust(a k, Tr i H [ti Z,t i ])) [0, 1 [ perceived impression= competition 3 ETrust(a k, Tr i H [ti Z,t i ])) [ 1 3, 2 ] perceived impression= coopetition 3 ETrust(a k, Tr i H [ti Z,t i ])) ] 2, 1] perceived impression= cooperation 3 A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 14 / 28

15 Trust model Dyadic Dimension Temporal Jøsang s trust model where: p = l=i (ac + jl l=0 n = l=i (ac jl l=0 b = p p + n + 1 e t i t l Z e t i t l Z d = n p + n + 1 u = ) is the number of positive actions ) is the number of negative actions 1 p + n + 1 b + d + u = 1 Evaluation DTrust(a k, a j, t i ) = T s T w T r b if u < θ if Ro k = cooperator if Ro k = competitor otherwise if Ro k = coopetitor where θ is an uncertainty threshold reflecting the reliability of the trust evaluation A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 15 / 28

16 Trust model Relationship Characterization Let λ inf and λ sup be respectively, the trust lower and the trust upper thresholds DTrust(a k, a j, t i ) be the dyadic trust value that the agent a k has in the agent a j at time period t i ρ : E R be a function that links directed edges to the relationships they represent DTrust(a k, a j, t i ) λ sup ρ((a k, a j )) = R coo DTrust(a k, a j, t i ) ]λ inf, λ sup [ ρ((a k, a j )) = R cop DTrust(a k, a j, t i ) λ inf ρ((a k, a j )) = R com A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 16 / 28

17 Social dynamics Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 17 / 28

18 Social dynamics Decision-making process At each time period t i of the simulation, every agent has to make the following decisions based on trust evaluation: 1 What is its potential for action? (i.e number of actions to perform) 2 With who to interact? (i.e. known or unknown agent) 3 How to interact with it? (i.e. positive or negative action) A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 18 / 28

19 Social dynamics Potential for action (Pa i k ) Let N k be he maximum number of possible actions that an agent a k can perform at each time period t i Role of agent a k Qualified perception competition coopetition cooperation competitor high Pak i (N k) medium Pak i ( Nk 2 ) low Pai k ( Nk 4 ) coopetitor medium Pak i ( Nk 2 ) high Pai k (N k) medium Pak i ( Nk 2 ) cooperator low Pak i ( Nk 4 ) medium Pai k ( Nk 2 ) high Pai k (N k) Table: Potential for action of an agent a k according to the qualified perception and its role A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 19 / 28

20 Social dynamics With who to interact Agents evolve in crowd innovation environment Agent a k interacts with: Known agents LKA k PIT k such as LKA k = α Pa i k cooperator: top trustworthy agents competitor: least trustworthy agents coopetitor: no matter Unknown agents LUA k V \ {PIT k } such as LUA k = (1 α) Pa i k A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 20 / 28

21 Social dynamics Action rules Role of ak Action type probability of positive action probability of negative action cooperator p + =1 p = 0 competitor p + =0 p = 1 co-opetitor p + = ETrust p = 1 ETrust Table: Actions rules between an agent a k and an unknown agent a j LUA k Relationship between a k and a j R Role of a com R cop R coo k competitor p + = 0, p = 1 p + = DTrust, p = 1 DTrust cooperator p + = DTrust, p = 1 DTrust p + = 1, p = 0 co-opetitor p + = 0, p = 1 p + = DTrust, p = 1 DTrust p + = 1, p = 0 Table: Trust-dependent actions rules between an agent a k and a known agent a j LKA k for the different relationship types A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 21 / 28

22 Experimental setup and results evaluation Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 22 / 28

23 Experimental setup and results evaluation Experimental setup n = 1000 N k = 4 α = 0.5 cardinal(pset i ) = cardinal(e i 1) 10 Z = 5 λ inf = 0.3 and λ sup = 0.7 Simulation duration: 50 time periods Roles distribution: homogeneous 33% of cooperators (C coo ), 33% of competitors (C com ) and 33% of coopetitors (C cop ) performance i (C l ) = {a k C l Pa i k > N k 2 } C l l {coo, com, cop} A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 23 / 28

24 Experimental setup and results evaluation Results Number of motivated agents Cooperator Competitors Coopetitors Time period Figure: The performance evolution in an homogeneous platform A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 24 / 28

25 Conclusion and Future Research Plan 1 Context 2 Concepts and Agent Architecture 3 Trust model 4 Social dynamics 5 Experimental setup and results evaluation 6 Conclusion and Future Research A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 25 / 28

26 Conclusion and Future Research Conclusion A multi-relational social network model Trust management model built upon two dimensions: environmental and dyadic to guide decision-making process of agents when they interact An agent-based model to simulate users behavior and understand social dynamics in homogeneous platform A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 26 / 28

27 Conclusion and Future Research Future Research Study new and not deterministic action rules Examine a dynamic roles distribution where individuals can change roles Perform an exploratory and structural analysis of obtained graphs Make more simulations while varying the parameters values. A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 27 / 28

28 Conclusion and Future Research Merci! Questions...? A.Louati Une approche multi-agentpour évaluer la performance des plateformes de crowdsourcing d idées 28 / 28