# INFO-FOM {% hint style="info" %} This INFO-FOM draft is shared for discussion purposes. It is not a standard and may change. {% endhint %} ### Why an INFO-FOM? Traditional FOMs such as **RPR-FOM** or the **NETN suite** provide strong foundations for interoperability in distributed simulation. However, they are limited when it comes to representing the **information environment** - the interplay of narratives, audiences, memory, and influence. Conducttr has developed an **INFO-FOM draft** to fill this gap. It enables simulations to model: * **Narratives and symbols** (the building blocks of influence) * **Audience states** (attitudes, beliefs, dispositions, morale) * **Memory and salience** (what information persists, what fades) * **Information events** (disinformation, media amplification, symbolic acts) This makes INFO-FOM a bridge between **cognitive modelling** and **HLA Evolved interoperability**, allowing information effects to be represented alongside kinetic and cyber effects. ### Relationship to Existing Standards INFO-FOM is designed to be **complementary**: * Extends **NETN-BASE** with focus on audience and narrative. ### Scope The INFO-FOM draft introduces extensions in three main areas: * **Audience and Actor State Representation** * **Narratives, Events, and Collective Action** * **Injects and Scenario Control** #### 1. **Audience and Actor State Representation** The module defines both **static descriptors** (identity, affiliation, role) and **dynamic state attributes** (morale, disposition, emotional state, memory, beliefs, attitudes) for **Audiences** (population groups) and **Actors** (individuals). * **AudienceState** includes morale, disposition level, emotional state, participation rate, belief and attitude structures, and symbolic memory traces. * **ActorState** mirrors this at the individual level, enabling fine-grained modelling of key personas or leaders. * Both use JSON-encoded fields to flexibly capture belief systems, attitudes, and memory states, while maintaining interoperability through HLA standard datatypes. This allows federates to track **how populations perceive, remember, and respond** to narrative events in real time. #### 2. **Narratives, Events, and Collective Action** The module defines a dedicated class of **narrative-driven interactions**: * **NarrativeEvent**: Represents messages, media broadcasts, symbolic acts, or zone triggers that may shift audience or actor states. Each event carries metadata such as origin, channel, emotional energy, and target audience. * **Event**: Provides an **objective, factual record** of occurrences, separate from narrative framing, ensuring traceability between injects and state updates. * **CollectiveAction**: Captures group behaviours (e.g., demonstrations, online campaigns, symbolic protests) and links them back to participating audiences. This makes it possible to simulate not only **information injects**, but also their **observable consequences** in terms of population behaviour. #### 3. **Injects and Scenario Control** To support experimentation and training, the FOM defines **inject interactions** that can alter state directly: * **AudienceStateInject** and **ActorStateInject** allow other platforms to directly modify morale, beliefs, attitudes, or memory. * **ScenarioInject** provides inject for **pre-planned event lists** (MEL/MIL) allows start, pause, resume, stop, reset. * **InjectResult** is an optional feedback mechanism to confirm inject has been received (suitable for debugging). This allows more direct control over audience & activity activity. ### Download {% file src="/files/dYIuzVrZSgthSen2OrIM" %} ### Next Steps We are sharing this draft to: * Invite feedback from the defence simulation and IO/StratCom community. * Encourage partners and clients to consider how **cognitive and narrative effects** can be simulated in HLA federations. If you’d like to discuss the INFO-FOM draft, please contact us at --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://helpdocs.conducttr.com/technical-reference/info-fom.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.