# 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="" %} ### 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