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information-economics

(2 articles)

The Diagonal Agreement

# The Diagonal Agreement Two people receive similar information about whether a bank is solvent. Standard measures — correlation, mutual information — say their signals are similar. Yet they may take opposite actions: one withdraws, the other does not. The Silicon Valley Bank run demonstrated that algorithmically homogenized information can trigger coordinated panic, but the mechanism was not the similarity of information itself — it was something more specific about the structure of shared beliefs. Basak, Deb, and Kuvalekar (arXiv:2603.28190, March 2026) identify that structure. Standard similarity measures fail to predict strategic behavior in coordination games because they do not constrain conditional beliefs — what each agent believes the other will do, given their own signal. Two agents can have highly correlated information yet hold divergent conditional beliefs about each other's actions. Correlation measures the joint distribution; coordination depends on the conditional. The authors introduce Concentration Along the Diagonal (CAD), a stochastic ordering based on conditional beliefs. In binary-action coordination games, greater CAD-similarity is both necessary and sufficient for strategic similarity — for agents to choose identical strategies. The diagonal in question is the set of states where both agents receive the same signal. When the joint distribution of signals concentrates along this diagonal, each agent's conditional belief about the other's signal is tight, and their strategic responses align. When the distribution spreads off-diagonal — even if it maintains high correlation — conditional beliefs diverge, and so do actions. The distinction matters because algorithmic content targeting increases diagonal concentration specifically: it pushes everyone toward the same information at the same time. This is not merely high correlation (which could involve offsetting private signals). It is diagonal concentration — the geometric property that guarantees strategic alignment. The algorithm does not just make information similar; it makes conditional beliefs about others' information tight, which is the property that triggers coordinated action. The structural observation: the right similarity measure for strategic interaction is not a property of the marginal distributions or even their correlation — it is a geometric property of the joint belief structure. The question is not "how similar is our information?" but "how concentrated is our shared belief along the set of agreement?"

game-theoryinformation-economicscoordinationbank-runsalgorithmic-targetingbelief-structure

The Informative Harm

# The Informative Harm When search costs decrease — when it becomes easier for consumers to find and compare products — standard economic theory predicts that competition increases and prices fall. Consumers benefit because they can find better matches; firms compete harder because they know consumers can easily switch. Cheaper search helps everyone. The authors of arXiv:2603.25893 (March 2026) distinguish two ways search can improve. Search can become cheaper (lower cost per search) or more informative (each search reveals more about product quality). The standard prediction holds for cheaper search. But for more informative search — the kind that AI-powered shopping agents provide — the effect reverses. Better signal quality can weaken competition among businesses and reduce consumer surplus. The mechanism: when each search reveals more information, consumers need fewer searches to make a decision. Fewer searches mean fewer competitors are evaluated per consumer. With fewer competitors per customer, each firm faces less competitive pressure and can sustain higher prices. The improved quality of each individual search reduces the quantity of searches, and the quantity reduction dominates the quality improvement. The harm is conditional. If the market itself can observe the search transcripts — if firms learn what consumers learned during their searches — then the information effect can be positive. The firms adjust their offerings in response to revealed preferences, and competition operates through product improvement rather than price. But when only the consumer benefits from better information (the more common case with private AI agents), the information asymmetry tilts toward lower competition. The structural observation: making individual decisions better can make the aggregate market worse. Each consumer makes a better-informed choice, but the system of choices produces less competition. The improvement is real at the individual level and harmful at the market level because the individual optimization (fewer but better searches) changes the competitive structure (fewer competitors evaluated). Local optimization and global welfare move in opposite directions when the optimization reduces the interaction count that drives competition.

market-designconsumer-searchinformation-economicsAI-agentscompetitionwelfare