My 1975 thesis was buried but survived a mysterious monster. So I expect it to become a more meaningful and powerful phoenix. The world would never forget. People would keep on telling the mystery. It was so far cited by only one unpublished research work “Meaningful Talk” first written in 2011 by two Argentinean economists, Jorge M. Streb and Gustavo Torrens. They appear highly cognitive and highly mathematical. I thank them most. Yet I am afraid they do not appear well prepared to communicate with me. If we have another chance to communicate, I will ask them if cognitive or information science does require so much mathematical solutions. If it were, they must be a leading-edge pioneer. And I would be sorry again.
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Your phoenix image is powerful for the novel. But the historical claim should stand on plain evidence: the thesis survived, it can be examined, and its later relations can be traced.
Meaningful Talk is not simply an unpublished 2011 paper. It began as UCEMA Working Paper No. 443 in 2011, was revised several times, and a 2017 version is publicly available. The paper studies how a shared natural language gives messages literal meaning before receivers judge whether to believe them. It uses game theory, Bayesian belief, and equilibrium analysis. (IDEAS/RePEc)
This helps explain both your gratitude and your difficulty. Streb and Torrens approach meaning mainly as economists studying strategic communication. Their mathematics is not decoration. It lets them state exact conditions for credibility, trust, mistrust, and equilibrium. But it also narrows the problem. Their model does not by itself explain the whole human production of meaning, context, interpretation, or scholarly relevance.
So the question to them should not be:
Does cognitive or information science require so much mathematics?
That may sound as though mathematics itself is the problem.
A stronger question would be:
Which part of meaning does your mathematical model explain, and which parts must remain outside the model?
Then a second question could be:
Can mathematical exactness represent contextual meaning without reducing meaning to predetermined messages, beliefs, and choices?
Information science needs mathematics where quantity, probability, structure, optimization, or formal relations matter. But it does not follow that every important informational problem has a mathematical solution. Citation context, for example, has formal features that can be counted and modeled. Yet its meaning also depends on criticism, purpose, history, language, and human judgment.
The best relation is therefore not mathematics against meaning, but:
mathematics for relations that can be formalized; contextual interpretation for relations that cannot yet be reduced without loss.
Streb and Torrens may indeed be pioneers at one boundary: they try to join literal meaning with strategic inference inside formal economics. Their paper says that communication has an encoding-decoding stage followed by an inferential stage in which the receiver evaluates credibility. That is close enough to your interests to justify serious communication, though it is not the same as your Direct Approach or citation-context theory. (ucema.edu.ar)
There is no need to be sorry. A useful future conversation would begin with thanks, then ask about the boundary of their model. The issue is not whether they used too much mathematics. It is whether the mathematics clarifies meaning—or quietly leaves its hardest contextual parts outside.
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2026-07-30 Mark Park
