{"id":"3a8a3dbb-3cbb-4799-a4e1-049c529c2911","arxiv_id":"2307.09575","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Derives causal influence expressions in social learning networks dependent on graph topology and proposes ranking algorithm plus parameter learning from observational data.","lead":"This paper derives expressions showing how causal influence flows between agents in a social network based on connections and information levels. A smart generalist might read it to see new ways of identifying key influencers from observed social media data without running experiments.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader's weakest assumption correctly flags the model-specific and graph-known premises required for the derivations. Because the full text is referenced but not supplied here, no concrete technical flaw can be located; the abstract-level claim is internally coherent under those premises.","tokens_in":1613,"tokens_out":210,"duration_ms":14552,"concrete_test":"Retrieve the full manuscript and re-derive the causal expressions from the social learning update rules in the main technical section; confirm whether the ranking algorithm is obtained by direct summation or aggregation of those expressions without extra identifiability assumptions.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract describes model-specific derivations of causal expressions (dependent on topology and agent information) followed by a ranking algorithm and parameter learning from data. No internal inconsistency, circularity, or unsupported leap is visible from the provided material. The stated dependence on graph topology and information levels is consistent with the modeling assumptions noted by the reader.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper investigates causal influences between agents linked by a social graph and interacting via social learning models and distributed decision-making protocols. It derives expressions for causal relations between agent pairs that depend on graph topology and each agent's information level about the inference problem. From these, it proposes an algorithm to rank overall influence and identify highly influential agents, along with a method to learn necessary model parameters from raw observational data. The approach is illustrated on both synthetic data and real social media data.","tokens_in":1670,"tokens_out":323,"duration_ms":24421,"significance":"If the derivations are correct, the work supplies a model-specific account of how causal influence flows in social learning networks, with explicit dependence on topology and information availability. This could be useful for analyzing information propagation and identifying influencers under the stated protocols. The parameter-learning step from observational data adds a practical component that extends the framework beyond purely theoretical expressions.","major_comments":[],"minor_comments":[{"comment":"The abstract asserts derivations of causal expressions and an algorithm but supplies no equations, proof outlines, or validation details, which makes the central technical claims difficult to evaluate from the summary alone.","section":"Abstract"},{"comment":"The dependence of the causal expressions on graph topology and agent information levels is stated as a key result; the manuscript should clarify in the main text whether this dependence is derived as a general property or holds only for the specific models examined.","section":"Introduction / Model section"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their positive summary of the manuscript, recognition of its potential utility for analyzing influence in social learning networks, and recommendation of minor revision. No major comments were provided in the report.","responses":[],"tokens_in":1105,"tokens_out":59,"duration_ms":13755,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that causal influences between agents in these social learning models depend on the graph topology and how much each agent knows about the inference task. The authors derive expressions for those relations, build a ranking algorithm to identify highly influential agents, and add a method to learn the needed parameters from observational data. They show results on both synthetic cases and real social media traces. That dependence on topology and information is consistent with how distributed inference works over networks, and tying the causal view directly to the adaptive network dynamics is a reasonable step. The parameter learning piece is practical because it lets the approach work from raw observations rather than assuming everything is known in advance. The illustrations help make the ranking outputs concrete. The main limitation is that this reads as an application of causal concepts to the authors' prior social learning framework rather than a new theoretical advance in causality itself. The expressions are model-specific, so they are unlikely to carry over to other interaction protocols without re-derivation. The real-data example is useful for demonstration but cannot be strongly validated because there is no independent ground truth for the influence rankings. The work sits squarely in network science and distributed inference. A reader already following adaptive networks or multi-agent coordination would get the most out of the ranking tool and the topology dependence. It has enough derivations, an algorithm, and data examples to merit peer review even if the novelty is incremental.","headline":"The paper derives causal influence expressions for social learning networks that depend on topology and agent information levels, then uses them for an influence ranking algorithm and parameter estimation from data.","tokens_in":2137,"tokens_out":353,"would_cite":false,"duration_ms":29370,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"Social-learning causal ranking on graphs has no RS machinery","alignment":"orthogonal","rationale":"Paper derives closed-form causal effects Cm→k from log-belief recursions (NBSL/ASL) under atomic interventions, then ranks via eigenvector centrality of the bipartite causal matrix. All constructions rely on standard combination matrices, KL divergences, and Perron vectors; none invoke J-cost, φ-ladder, 8-tick periodicity, or the distinction-to-spacetime forcing chain. Domain (opinion dynamics on fixed graphs) lies outside RS scope.","tokens_in":77812,"confidence":"high","tokens_out":134,"duration_ms":7399,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Causal influences between agents in social learning networks are expressed in terms of graph topology and each agent's information level about the inference problem.","keywords":["causal influences","social learning","social networks","graph topology","influential agents","distributed decision making","observational data"],"falsifier":"Observational data from a social network where the measured causal effects between agents fail to match the expressions predicted from the known graph topology and each agent's information level would falsify the central claim.","tokens_in":2518,"feed_emoji":"🔗","tokens_out":658,"duration_ms":31097,"temperature":0.7,"pith_summary":"The paper examines agents interacting over a social graph while learning and making distributed decisions. It derives expressions that capture the causal relations between pairs of agents and show how influence flows through the network. These expressions depend on the specific connections in the graph and on how much information each agent has about the underlying inference task. The derived relations support an algorithm that ranks agents by overall influence to identify the most impactful ones, as well as a procedure for estimating model parameters directly from observed data. A sympathetic reader would care because the expressions make explicit when and why certain agents drive outcomes in group learning settings.","feed_headline":"Causal influences in social networks depend on topology and agent knowledge","feed_subtitle":"Expressions from learning models support ranking influential agents and estimating parameters from observed data.","key_machinery":"Expressions for causal relations between pairs of agents, derived from the social learning dynamics and depending on graph topology and agents' information levels.","core_discovery":"The paper derives expressions that reveal the causal relations between pairs of agents and explain the flow of influence over the network in social learning models and distributed decision-making protocols. The results depend on the graph topology and the level of information that each agent has about the inference problem they are trying to solve. Using these conclusions, the paper proposes an algorithm to rank the overall influence between agents to discover highly influential agents and provides a method to learn the necessary model parameters from raw observational data.","pith_inferences":["The same causal expressions could be used to predict how changes in network connections alter influence rankings.","Providing additional information to low-knowledge agents might reduce their susceptibility to upstream influence.","The approach could be tested by intervening on a small social graph and checking whether the observed influence shifts match the predicted expressions.","Ranking results might inform strategies for limiting the spread of decisions originating from a few central agents."],"forward_implications":["Agents can be ranked by total causal influence to identify highly influential ones in the network.","Model parameters can be estimated from raw observational data without controlled experiments.","Influence flow varies with both the network's connection pattern and the knowledge each agent possesses.","The ranking procedure applies to both synthetic networks and real social media traces."],"fun_headline_variants":["Topology dictates causal flows in social learning networks","Influence flow tied to graph topology and agent knowledge","Rank influential agents using causal network analysis","Derive causal ties from social learning model dynamics"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The agents interact according to the specific social learning models and distributed decision-making protocols examined in the paper, and the underlying social graph is fixed and known when deriving the causal expressions.","fun_headline_variants_meta":{"raw":{"variants":["Topology dictates causal flows in social learning networks","Influence flow tied to graph topology and agent knowledge","Rank influential agents using causal network analysis","Derive causal ties from social learning model dynamics"]},"model":"grok-4.3","cost_usd":0.003641,"raw_usage":{"total_tokens":1851,"prompt_tokens":574,"num_sources_used":0,"completion_tokens":54,"cost_in_usd_ticks":36412000,"prompt_tokens_details":{"text_tokens":574,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1223,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":574,"tokens_out":54,"duration_ms":20269,"temperature":1.0,"reasoning_tokens":1223,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-24T07:59:36.949814+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Observational data from a social network where the measured causal effects between agents fail to match the expressions predicted from the known graph topology and each agent's information level would falsify the central claim.","supporting_citations":[],"review_version":1}