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The simple gesture of pointing can greatly augment ones ability to comprehendstates of the world based on observations. It triggers additional inferencesrelevant to ones task at hand. We model an agents update to its belief of theworld based on individual observations using a partially observable Markovdecision process (POMDP), a mainstream artificial intelligence (AI) model ofhow to act rationally according to beliefs formed through observation. On topof that, we model pointing as a communicative act between agents who have amutual understanding that the pointed observation must be relevant andinterpretable. Our model measures relevance by defining a Smithian Value ofInformation (SVI) as the utility improvement of the POMDP agent before andafter receiving the pointing. We model that agents calculate SVI by using thecognitive theory of Smithian helping as a principle of coordinating separatebeliefs for action prediction and action evaluation. We then import SVI intorational speech act (RSA) as the utility function of an utterance. These leadus to a pragmatic model of pointing allowing for contextually flexibleinterpretations. We demonstrate the power of our Smithian pointing model byextending the Wumpus world, a classic AI task where a hunter hunts a monsterwith only partial observability of the world. We add another agent as a guidewho can only help by marking an observation already perceived by the hunterwith a pointing or not, without providing new observations or offering anyinstrumental help. Our results show that this severely limited and overloadedcommunication nevertheless significantly improves the hunters performance. Theadvantage of pointing is indeed due to a computation of relevance based onSmithian helping, as it disappears completely when the task is too difficult ortoo easy for the guide to help.

Individual vs. Joint Perception: a Pragmatic Model of Pointing as Communicative Smithian Helping