Reflecting on Social Influence in Networks

Christoff, Zoé; Hansen, Jens Ulrik; Proietti, Carlo

doi:10.1007/s10849-016-9242-y

Cited by 28 publications

(21 citation statements)

References 32 publications

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“…Socalled threshold models, having gained early, wide-spread attention through [18,33,34], are used precisely to represent the dynamics of diffusion under threshold-limited influence. This type of models has received a lot of attention in the recent literature [1,15,17,20,23,25,26,36], also within the logic community [8][9][10][11][12]24,28,29,32,35,39]. This paper has two goals.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Epistemic Logics of Diffusion and Prediction in Social Networks

et al. 2018

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We take a logical approach to threshold models, used to study the diffusion of opinions, new technologies, infections, or behaviors in social networks. Threshold models consist of a network graph of agents connected by a social relationship and a threshold value which regulates the diffusion process. Agents adopt a new behavior/product/opinion when the proportion of their neighbors who have already adopted it meets the threshold. Under this diffusion policy, threshold models develop dynamically towards a guaranteed fixed point. We construct a minimal dynamic propositional logic to describe the threshold dynamics and show that the logic is sound and complete. We then extend this framework with an epistemic dimension and investigate how information about more distant neighbors' behavior allows agents to anticipate changes in behavior of their closer neighbors. Overall, our logical formalism captures the interplay between the epistemic and social dimensions in social networks.

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Section: Introductionmentioning

confidence: 99%

Dynamic Epistemic Logics of Diffusion and Prediction in Social Networks

et al. 2018

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“…We leave general characterizations of equi-expressive fragments of GDDL and DTML as open question, but remark that all GDDL action-examples of [22] may be emulated using DTML action models, and in many cases via fairly simple ones. More thoroughly, we show that the logic of Knowledge, Diffusion and Learning (KDL, [12]) has a complete and decidable system, a question left open in [12]. This is shown by encoding KDL in DTML.…”

Section: Learning Whomentioning

confidence: 89%

“…The static hybrid languages of [9][10][11][12]19,21,22] are all sub-languages of L(P, N om), defined and translated into DTML below. [18] also includes state nominals, which our results do not cover.…”

Section: Embedding Static Languages and Modelsmentioning

confidence: 99%

“…We show that KDL [12] dynamics may be embedded in DTML, for finite agent sets (as assumed in [12]). Given Prop.…”

Section: Kdl Dynamic Transformations and Learning Updates In Dtmlmentioning

confidence: 99%

“…The static language of [12] is then L −U (FP, N om). The dynamic language L KDL extends L −U (FP, N om) with dynamic modalities [d] and [ℓ] for dynamic transformations d and learning updates ℓ:…”

Section: Kdl Dynamic Transformations and Learning Updates In Dtmlmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Term-Modal Logic for Epistemic Social Network Dynamics

Liberman¹,

Rendsvig

2019

Logic, Rationality, and Interaction

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Logics for social networks have been studied in recent literature. This paper presents a framework based on dynamic term-modal logic (DTML), a quantified variant of dynamic epistemic logic (DEL). In contrast with DEL where it is commonly known to whom agent names refer, DTML can represent dynamics with uncertainty about agent identity. We exemplify dynamics where such uncertainty and de re/de dicto distinctions are key to social network epistemics. Technically, we show that DTML semantics can represent a popular class of hybrid logic epistemic social network models. We also show that DTML can encode previously discussed dynamics for which finding a complete logic was left open. As complete reduction axioms systems exist for DTML, this yields a complete system for the dynamics in question.

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