The Development of Communication Models

Reference: Kuznar, L. A. & Yager, M. (2020). The Development of Communication Models (Quick Look). NSI, Inc., prepared for the Department of Defense Strategic Multilayer Assessment (SMA) — Integrating Information in Joint Operations (IIJO) project. October 2020. DTIC accession AD1118283. DTIC citation · PDF · NSI.

Summary

This SMA “Quick Look” traces the intellectual history of communication models — the schematic diagrams that decompose the act of communication into elements (sender, message, channel, receiver, noise, feedback, context) and the relations among them. Its purpose is applied rather than theoretical: the review supplies the conceptual baseline for SMA’s Effective Communication Framework, which the Department of Defense uses to reason about the Information Environment (IE) and about how “information influences behavior through the process of communication.” Understanding the models is presented as a prerequisite for building meaningful measures of effectiveness (MOEs) for influence and information operations, rather than mere measures of performance (MOPs).

The report organises the field as a progression through three families. Linear models (e.g. Lasswell’s 1948 “who says what to whom in what channel with what effect”, and the Shannon–Weaver sender→encoder→channel→decoder→receiver-with-noise diagram derived from A Mathematical Theory of Communication) treat communication as a one-directional transmission from a source to a passive audience. Interactive models (e.g. Schramm/Osgood) add feedback and a shared field of experience, recognising that sender and receiver alternate roles and that a message is only understood insofar as their interpretive frames overlap. Transactional models go further, treating the parties as simultaneous senders-and-receivers who co-construct meaning within social, relational, and cultural context, so that “the information environment” itself expands as each new model admits more variables and interactions. The authors read this trajectory as a steady enrichment: successive models internalise more of the context that determines whether a message actually changes an audience’s perceptions, attitudes, and ultimately behaviour.

Key Ideas

Communication models as decomposition tools: the lines, arrows, and shapes each stand for a distinct element or interaction, letting analysts isolate and measure parts of the communication process.
Three-stage historical arc: linear (transmission) → interactive (feedback + shared field of experience) → transactional (simultaneous, context-embedded co-construction of meaning).
Lasswell’s model: the compact “who / says what / in which channel / to whom / with what effect” formulation of one-way mass communication.
Shannon–Weaver model: the engineering transmission diagram (source, transmitter, channel, noise, receiver, destination) adapted from information theory into human communication.
Interactive turn (Schramm/Osgood): feedback loops and overlapping fields of experience make shared meaning, not mere transmission, the criterion of success.
Transactional turn: parties send and receive at once; relationship, culture, and situation are constitutive, not incidental.
Applied framing: models underpin SMA’s Effective Communication Framework and the shift from MOPs to MOEs when assessing influence in the Information Environment.

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Conceptual Contribution

Claim: Communication is best understood not as one-way transmission but as an increasingly context-rich, bidirectional, meaning-constructing transaction; the more variables of the information environment a model captures, the better it predicts whether communication actually shifts behaviour.
Mechanism: A historical survey of canonical models (Lasswell, Shannon–Weaver, Schramm/Osgood, transactional) that decomposes each into its elements and reads their succession as progressive enrichment of the represented context; the synthesis feeds SMA’s Effective Communication Framework and its MOP→MOE distinction.
Concepts introduced/used: Information Environment, Influence Operations, Transactional Communication, Feedback, Shared Field of Experience, Measures of Effectiveness
Stance: survey / applied policy framework
Relates to: grounds the human-communication side of the same transmission problem Shannon formalises in A Mathematical Theory of Communication; the “meaning is co-constructed in context” thesis parallels the intent- and convention-based accounts of meaning that motivate agent communication languages (e.g. Communicative Actions for Artificial Agents - Cohen Levesque).

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Communicative Actions for Artificial Agents - Cohen Levesque

Communicative Actions for Artificial Agents

Reference: Cohen, P. R. & Levesque, H. J. (1995). Communicative Actions for Artificial Agents. In Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), San Francisco, June 1995, pp. 65–72. AAAI Press. Reprinted in J. M. Bradshaw (ed.), Software Agents (AAAI Press / MIT Press 1997), ch. 19. URL (AAAI PDF)

Summary

This paper is the high-water mark of mentalistic ACL semantics: the most explicit, systematic statement of the position that an agent communication act is a complex operator on the BDI states of sender and receiver, and that the semantics of an ACL performative is given by feasibility preconditions and rational effects expressed in modal logic over those mental states. Cohen and Levesque extend the Cohen–Levesque 1990 modal logic of intention into a logic of rational action in which speech acts are first-class actions whose definitions are derivable from more basic actions of attempting to bring about a state of affairs, together with assumed Gricean rationality on the part of both agents. The standard FIPA-ACL semantics in the FIPA Communicative Act Library Specification (FIPA00037) is essentially a streamlined adaptation of the Cohen–Levesque framework.

The mechanism is precise. A speech act e performed by speaker s toward hearer h has a feasibility precondition (e.g. s believes p, s believes h does not know p, s intends h to come to know p) and a rational effect (e.g. h believes p because h believes s sincerely informed). The semantics of inform, request, promise, commit, propose, and other performatives are derived by composition from the more primitive attempt: a request is an attempt to bring about that the hearer perform an action and that the hearer believe the speaker wants the action performed; an inform is an attempt to bring about that the hearer come to believe p and to know the speaker believes p; and so on. The framework is mathematically beautiful and ontologically heavy: it requires every speech act to be defined in terms of nested beliefs and intentions of speaker and hearer, often to several levels of nesting.

This is precisely why Singh 1998 writes the Rethinking critique. The Cohen–Levesque definitions are unverifiable in practice: no external observer can check that an agent believed p when it sent inform(p); the inferential web that fixes whether the agent “really” performed an inform speaks entirely about the agent’s mental state, which is exactly the inscrutable thing the protocol participants do not have access to. The paper is therefore the canonical negative example for CBCL and the commitment-based tradition: it shows what a fully worked-out mentalistic semantics looks like, and that shows why the field has had to move on. The right way to read it in 2026 is as the high statement of a coherent but unverifiable position, not as a blueprint to implement.

Key Ideas

Speech act = action on mental states: a communicative act is a complex action whose semantics is given by feasibility preconditions and rational effects expressed over the BDI states of speaker and hearer.
Attempt as the primitive: more elementary than the standard speech-act categories is the action of attempting — an agent attempts an action toward an end if and only if it performs the action with that goal and the belief that the action is the means.
Composition of performatives: standard performatives (inform, request, promise, propose, confirm, agree, refuse) are derived by composing attempt with specific mental-state preconditions and goals.
Sincerity and competence as background assumptions: the rational effects only follow if speaker and hearer are sincere (no lying) and competent (no false beliefs). When these fail, the framework’s predictions silently fail too.
Joint intentions in conversation: extends the 1990 framework with joint intentions — for conversational acts like agreement, both parties acquire a shared intention with mutual belief.
Verifiability is a known cost: the paper acknowledges that the mental-state preconditions are not externally observable; it argues this is the price of a real semantics rather than a behavioural surrogate.
Closest formal ancestor of FIPA: the FIPA-ACL CAL spec (FIPA00037) is a direct adaptation of this framework with light editorial smoothing.

Connections

Conceptual Contribution

Claim: Communicative acts can be given precise semantics by defining them as complex actions on the BDI states of sender and receiver, derived by composition from the primitive notion of attempt under shared rationality assumptions. The semantics of an ACL performative is its feasibility precondition and rational effect, expressed in modal logic over beliefs, goals, and intentions.
Mechanism: Extend the Cohen–Levesque 1990 modal logic of intention with a notion of attempt (an agent attempts an action toward an end iff it performs the action with that goal and the belief that the action serves the goal). Define each standard performative by composition: inform(s,h,p) requires s believes p, s believes h does not know p, s intends h to come to know p; the rational effect is that h comes to believe p by mutual recognition of s’s sincerity. Similar definitions for request, promise, propose, agree, refuse, with joint-intention machinery handling conversational acts.
Concepts introduced/used: Attempt (Cohen-Levesque), Feasibility Precondition, Rational Effect, Mentalistic Semantics, Joint Intentions, Sincerity Condition, Communicative Act Library.
Stance: foundational technical paper / formal-semantics manifesto.
Relates to: The closest formal ancestor of the FIPA Communicative Act Library (FIPA00037) — the FIPA spec is essentially a streamlined adaptation of this framework. It is the negative example against which the public-semantics / commitment-based line defines itself: Singh 1998 argues precisely that the BDI preconditions are unverifiable and that ACL semantics should be grounded in observable social commitments instead. Yolum & Singh 2002, Fornara & Colombetti 2004, and Singh 1999 develop the public alternative. CBCL then operationalises that alternative as a verified, trace-checkable, self-extending wire format — explicitly not what Cohen and Levesque built. The philosophical critique of mentalistic semantics runs deeper, through Sellars / Brandom / <em>MIE</em> — Cohen–Levesque takes mental states as pre-given semantic primitives, which is the move inferentialism rejects.

A Mathematical Theory of Communication

Reference: Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), pp. 379–423 and 27(4), pp. 623–656. (Republished 1949 with additional commentary by Warren Weaver as The Mathematical Theory of Communication, University of Illinois Press.) DOI · Open access PDF (Harvard) · Internet Archive (BSTJ scan)

Summary

Shannon’s two-part 1948 paper founds information theory as a discipline. The setting is the engineering problem of communication: a source produces messages, an encoder transforms them into signals over a noisy channel, a decoder attempts to reconstruct the original. Shannon’s first move is to argue that the meaning of messages is irrelevant to the engineering problem — only their statistical structure matters. He then develops the foundational notions: entropy H = -Σ p_i log p_i as the average information content per symbol of a source; mutual information I(X;Y) = H(X) - H(X|Y) as the information one variable carries about another; channel capacity C = max I(X;Y) as the supremum of mutual information over all input distributions. The technical heart consists of two coding theorems. Source coding (noiseless coding): any source with entropy H can be losslessly compressed at rate arbitrarily close to H bits per symbol, but no lower. Channel coding (noisy-channel coding): any source with entropy below the channel capacity C can be transmitted with arbitrarily low error probability using sufficient block length, but transmission above C necessarily incurs error. Together these establish the operational meanings of entropy and capacity and bound what any communication system can achieve. The companion Mathematical Theory of Communication (1949) adds Weaver’s expository introduction, popularising the framework and inaugurating the broader engagement of philosophy and the social sciences with information theory. Shannon’s framework supplies the technical foundation for every communication system, the conceptual foundation for algorithmic information theory and the MDL principle, and a recurring background reference in agent-communication design — most explicitly in Why AI Agents Communicate In Human Language, which frames the case against natural-language inter-agent communication in Shannon-theoretic terms (lossy channel, low capacity per token, ambiguous code).

Key Ideas

Engineering decoupling from meaning: the engineering problem of communication is independent of semantic content; only the statistical structure of the source matters.
Entropy as average information: H(X) = -Σ p_i log_2 p_i measures the average uncertainty of a random variable in bits; for an i.i.d. source emitting symbols with probabilities p_i, H is the lower bound on bits-per-symbol for lossless compression.
Mutual information: I(X;Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) symmetrically measures how much knowing Y reduces uncertainty about X (and vice versa).
Channel capacity: C = max_{p(x)} I(X;Y) — the maximum mutual information achievable over a memoryless channel with input distribution p(x). Capacity is the operational supremum on reliable transmission rate.
Source coding theorem: lossless compression can achieve rate arbitrarily close to H (entropy) but no lower; the practical realisation is via Huffman coding, arithmetic coding, and variants.
Noisy channel coding theorem: for any rate R < C, there exists a coding scheme achieving error probability arbitrarily close to zero with sufficient block length; for R > C, error probability is bounded away from zero. The result is non-constructive — Shannon’s proof uses random coding — but the existence guarantee is what made coding theory a field.
Continuous channels and AWGN capacity: the second part extends the discrete results to continuous channels, deriving the famous C = B log(1 + S/N) for additive-white-Gaussian-noise channels of bandwidth B and signal-to-noise ratio S/N.
Ergodic processes and Markov sources: Shannon’s analysis carefully extends from i.i.d. to ergodic and Markov sources, motivating the asymptotic equipartition property (AEP) and laying the groundwork for source coding of non-i.i.d. data.

Connections

Conceptual Contribution

Claim: The engineering problem of communication is mathematically separable from semantics; the right primitives are entropy (uncertainty per symbol of a source), mutual information (information one variable carries about another), and channel capacity (supremum of reliable transmission rate over a noisy channel). Two coding theorems establish the operational meaning of these quantities — entropy as the lossless-compression lower bound, capacity as the reliable-transmission upper bound.
Mechanism: Probabilistic model of source / channel / receiver; definition of entropy, conditional entropy, joint entropy, and mutual information; noiseless source-coding theorem (lossless compression to within ε of H); noisy channel-coding theorem (reliable transmission below C, error-bounded above C); extension to continuous channels and Gaussian noise.
Concepts introduced/used: Shannon Entropy, Shannon Information, Mutual Information, Channel Capacity, Source Coding Theorem, Noisy-Channel Coding Theorem, Asymptotic Equipartition Property.
Stance: founding paper of an entire discipline.
Relates to: Direct technical predecessor of Kolmogorov complexity / Algorithmic Information Theory (Solomonoff, Kolmogorov, Chaitin 1960s) — where Shannon measures the average information of an ensemble, AIT measures the absolute information of an individual object as the length of its shortest description; both are operationally founded on the same coding-theorem intuition. Shannon’s deliberate decoupling of the engineering problem from semantics inaugurates the running tension in agent communication between Shannon information (statistical, channel-bounded) and meaningful information (semantic, conventional, illocutionary). The agent-communication-language tradition exists precisely to address what Shannon’s framework deliberately set aside; nevertheless, the engineering bounds Shannon establishes constrain any agent communication channel, including LLM-mediated natural-language exchange. Why AI Agents Communicate In Human Language makes this explicit: natural language as inter-agent code has low channel capacity per token, high error probability under lossy LLM compression, and ambiguous decoding — Shannon-theoretic objections that motivate the case for structured ACLs. Conceptually adjacent to Chomsky 1956, which addresses the generative-grammatical structure of language as a parallel layer to Shannon’s statistical structure; together they delimit the design space of any communication system.

The Development of Communication Models

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Key Ideas

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