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Mind-Reading vs. Simulation in Epistemically
Heterogeneous Social Networks
Gerhard Schaden
To cite this version:
Gerhard Schaden. Mind-Reading vs. Simulation in Epistemically Heterogeneous Social Net-works. Linking Social Effects in Language Processing to Social Effects in Language Evolution,Sep 2016, Nimegue, Netherlands. 2016. <hal-01369378>
HAL Id: hal-01369378
http://hal.univ-lille3.fr/hal-01369378
Submitted on 20 Sep 2016
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Mind-Reading vs. Simulationin Epistemically Heterogeneous
Social NetworksGerhard Schaden
Université de Lille & CNRS UMR 8163 STL
Mind-Reading vs. Simulationin Epistemically Heterogeneous
Social NetworksGerhard Schaden
Université de Lille & CNRS UMR 8163 STL
Background:¿e Nature of Pragmatic Inference
•Pragmatics concerns context-dependent inferences (generally assumed to be linked torational use of language by situated agents)
•How is this done (beyond and independent of particular algorithms, e.g., Gricean conversa-tional maxims, relevance theory or argumentation theory)?
Mind-Reading
(see, e.g., Sperber and Wilson, 2002)– Figure out epistemic state of interlocutors–Determine inferences based on inferredepistemic state of addressee
Simulation¿eory
(see, e.g., Carruthers and Smith, 1996, p. 3)–Assume that interlocutor has same epis-temic state as yourself
– Simulate likely inferences
•Di�erence might matter when agents’ epistemic contexts are not identical, that is, whenthey do not know and believe the same things (in real life: always)
•Not clear to which degree Mind-Reading is assumed to be psychologically real•Mind-Reading is slow and error-prone (especially when agents share little common ground)
Epistemically Heterogeneous Social Networks
•Humans are an unusually social and cooperative species (for primates). As a consequence,all langage learning (and most of language use) takes place in social networks.
Linguistic theory is concerned primarily with an ideal speaker-listener, in a completelyhomogeneous speech-community [. . . ] (Chomsky 1965, p. 3)
•¿is position necessarily ignores everything related to variation•Variation is a key ingredient in language change•Two kinds of heterogeneity will be investigated:– contact in social networks; and– partly di�ering epistemic contexts.
Reinforcement Learning with Polya Urns
•Polya-Urns provide a mathematical modelof reinforcement learning.
•Randomly draw a ball from the urn.• If the ball corresponds to the correct answer,a further ball will be added to the urn.
URNtwhite:1red:1
URNt+1white:2red:1
¿e probability of drawing ‘‘white’’ rises from 0.5 to 0.6
Learning Internally Di�erentiated Lexical Items
• I assume internally di�erentiated lexical representations like Pustejovsky’s qualia-structure.Lexical Usage Pro�le of an Agent
is represented as array of pondered submeanings with respect to these 2 words:
W1Q1 W1Q2 W1Q3 W1Q4 W2Q1 W2Q2 W2Q3 W2Q4Ag 1 1000 1000 1000 1000 1000 1000 1000 1000Ag 2 2000 2000 2000 1 1 1 1 2000
• Scenario:–Two words are absolute synonyms (see Skyrms, 2010): any draw = success–Each submeaning is an independent Polya urn (balls correspond to Word1 &Word2)– Speaker draws a word, and signals to hearer– Speaker & Hearer update weights for the chosen word
Mutation
•At some point in simulation: change in the surrounding world → agents adapt lexicalrepresentations
• In a submeaning, two types are distinguished (Type1 keeps weight; Type2 initialized at 1)• Instead of four submeanings, agents discriminate �ve di�erent submeanings•Epistemic state of mutants is superset of epistemic state of non-mutants
General Pattern: Absence of Mutation vs. Mutation (Regardless of Inference Method)
Diff
Ag1
Ag2
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Ag5
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Lexical Distances: 9 Agents in 3 Cliques
Lexi
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PICIWCDCACCWCPCCDistance=1Distance=2
Without Mutation
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Island 1
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Island 3
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Lexical Distances: 9 Agents in 3 Cliques
Lexi
cal D
ista
nce
PICIMCWINMCWDCACCWNMCCCWMCPCC
Mutation of Island 2
Pragmatics in Production
Mind-Reading Inferences
Agents discard for production parts of their own epistemic state the interlocutor lacks
Simulation InferencesAgents always take into account their full epistemic state for production
Signi�cant di�erences (p-values from Kruskal-Wallis test for network of 3 × 10 agents):DCA1 CCWNMC1 CCWMC1 PCC1 DCA2 PCC2
1.54385e-09 1.689598e-49 0.008774732 8.998603e-12 0 4.782354e-09
Mind-Reading Increases Lexical Distance
•With Mind-Reading inference, the lexicaldistance between agents of di�erent islandsis bigger than with Simulation inference
•Because Mind-Readers discard non-sharedepistemic states, they leave a smaller foot-print of their epistemic di�erences
•All things being equal, the less agents takeinto account other’s epistemic states, themore similar they become
DCA1−Sim DCA1−Sim DCA1−MR DCA1−MR
0.0
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1.0
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2.0
Lexical Distance: Simulation vs. Mindreading
Lexi
cal D
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SimulationMind−Reading
Acknowledgments & Sample ReferencesAll simulations have been performed with sbcl Common Lisp, using the graph-library by Eric Schulte (https://github.com/eschulte/graph). Networks have been drawn with graphviz (Gansner and North, 2000). Data analysis has been performed with GNU R.[1] Carruthers, P. and P. K. Smith, eds. (1996).¿eories of ¿eories of Mind. Cambridge: Cambridge University Press. [2] Chomsky, N. (1965). Aspects of the ¿eory of Syntax. Cambridge, MA: MIT Press. [3] Gansner, E. R. and S. C. North (2000). “An Open Graph VisualizationSystem and its Applications to So ware Engineering”. In: So ware — Practice and Experience 30.11: pp. 1203–1233. [4] Mühlenbernd, R. and M. Franke (2012). “Signaling Conventions: Who Learns What Where and When in a Social Network”. In:¿e Evolution of Language:Proceedings of EvoLang9. Ed. by T. C. Scott-Philips et al. Singapore: World Scienti�c: pp. 242–249. [5] Paul, H. (1995). Prinzipien der Sprachgeschichte. 9th ed. Tübingen: Niemeyer. [6] Pustejovsky, J. (1995).¿e Generative Lexicon. Cambridge: MIT Press. [7] Schaden, G. (2014).“Markedness, Frequency and Lexical Change in Unstable Environments”. In: Proceedings of the Formal & Experimental Pragmatics Workshop. Ed. by J. Degen, M. Franke, and N. Goodman. ESSLLI. Tübingen: pp. 43–50. [8] Skyrms, B. (2010). Signals. Evolution, Learning, &Information. Oxford: Oxford University Press. [9] Sperber, D. and D. Wilson (2002). “Pragmatics, Modularity and Mind-Reading”. In:Mind & Language 17.1–2: pp. 3–23.