Simulation refers to creating a computer model or program that imitates or emulates the behavior of a real-world system or process with something virtual, like a <a href="https://www.culturepulse.ai/lexicon/digital-twin" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">digital twin</a>. Simulation is most frequently used to study the behavior of complex systems that are difficult or impossible to observe directly. For example, <a href="https://www.culturepulse.ai/lexicon/agent-based-modeling" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">agent-based modeling</a> (ABM) and <a href="https://www.culturepulse.ai/lexicon/agent-based-modeling" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">multi-agent AI</a>, ABM’s subset, can be used within a simulation to study agent interactions within a more extensive simulated system or environment.

Simulation

Agent-based modeling (ABM) is a computational modeling technique where individual AI agents, programmed with specific behavior rules, interact with and change their environment within the model — including by interacting with other agents. ABM is a type of <a href="https://www.culturepulse.ai/lexicon/simulation" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">simulation</a> that helps make sense of complex systems from the ground up. Scientists frequently use modeling programs to see how agents do different things and make different choices. Simplified as such, modeling helps us understand complex systems like how traffic flows in a city, how a disease spreads through a population that is or isn’t wearing masks, or even how a colony of ants or a box full of toy people could work together. In his seminal paper on <a href="https://www.researchgate.net/publication/276913619_Method_Theory_and_Multi-Agent_Artificial_Intelligence_Creating_computer_models_of_complex_social_interaction" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">creating computer models of complex social interaction</a>, <a href="https://www.culturepulse.ai/team" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Justin E. Lane</a> writes extensively about ABM and its subset, <a href="https://www.culturepulse.ai/lexicon/multi-agent-ai" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">multi-agent AI</a> (MAAI). You can think about it this way: Imagine a toy box filled with lots of little toy people, and each toy person is an agent. Each agent can do different things and make different choices. When you take all the agents out of the toy box and let them play together, they can form different groups and even do different things with each other, like build a tower or play a game.

Agent-Based Modeling

Multi-agent AI (MAAI) is a subset of <a href="https://www.culturepulse.ai/lexicon/agent-based-modeling" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">agent-based modeling</a> (ABM). The key difference between the two is that MAAI’s agents are not only behaviorally realistic but also psychologically realistic. The term MAAI was coined by <a href="https://www.culturepulse.ai/team" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Justin Lane</a> in his 2014 paper <a href="https://www.researchgate.net/publication/276913619_Method_Theory_and_Multi-Agent_Artificial_Intelligence_Creating_computer_models_of_complex_social_interaction" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Method, Theory, and Multi-Agent Artificial Intelligence</a>, in which he studied religion, culture, and other complex social systems. MAAI opens modeling up beyond fixed, behavioral ABM agents. With MAAI, you can model dynamic societies that change and grow like real ones by creating a social <a href="https://www.culturepulse.ai/lexicon/digital-twin" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">digital twin</a>. A digital twin is imbued with psychologically realistic capabilities and beliefs, which give greater insight into predictable human or market behavior. <a href="https://www.culturepulse.ai/team" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">F. LeRon Shults</a>, CulturePulse co-founder, Professor of the Department of Global Development and Community Planning and the University of Agder, and founding member of the Center for Artificial Intelligence Research at the University of Agder, has this to say: <blockquote>One of the things that has changed is an acceptance that you really can model humans. Our agents are cognitively complex. They are simulated people with genders, ages and personalities. They can get married, have children, get divorced. They can get a job or get fired, they can join groups, they can die. They can have religious beliefs. They’re social in the way humans are. They interact with each other in social networks. They learn from each other, react to each other and to the environment as a whole.*</blockquote> *<a href="https://www.newscientist.com/article/mg24332500-800-ai-can-predict-your-future-behaviour-with-powerful-new-simulations/" rel="noopener noreferrer" target="_blank">New Scientist: AI can predict your future behaviour with powerful new simulations</a>

Multi-Agent AI

Resonance happens when an amplified information’s frequency matches another natural frequency of vibration. Because of matched vibrations, resonance is frequently associated with music. Behind the musical analogy, there’s a scientific explanation. For example, when you pluck a guitar string, it vibrates depending on its length, how it’s wound, its mass, and how hard you plucked it. If another guitar string of the same length and tension is nearby, it will vibrate too, but at a lower amplitude because it has a different mass. Once you step away from the science of resonance, think about it this way: two musical notes that sound more beautiful together than apart or water rippling across a pond. As a force, resonance can move fire, sand, and more.&nbsp; From a marketing perspective regarding resonance, consider those two notes that sound together and your messaging. First, marketers must pay close attention to the note their market plays and hear it clearly. Next, the marketer or advertiser must play a note that resonates with their target audience. Play the wrong one, and you risk alienating your market. Play the right one, and it will resonate, spreading farther and wider across your audience. You can find that note by testing your messaging on a CulturePulse <a href="https://www.culturepulse.ai/lexicon/digital-twin" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">digital twin</a>. For each message you test, your <a href="https://www.culturepulse.ai/lexicon/resonance-score" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Resonace Score</a> will tell you how much positive resonance your message has.

Resonance

Machine learning (ML) is a field of artificial intelligence (as is <a href="https://www.culturepulse.ai/lexicon/multi-agent-ai" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">multi-agent AI</a> and <a href="https://www.culturepulse.ai/lexicon/agent-based-modeling" rel="noopener noreferrer" target="_blank">agent-based modeling</a>) that focuses on computer systems using algorithms and statistical models to learn to program themselves on a particular task without explicitly being programmed to do so. ML algorithms train on data to learn patterns and relationships to formulate new data. This process involves feeding large amounts of data into an algorithm, adjusting its parameters, and assessing its performance until it can make accurate predictions or decisions without further adjustment. The difference between ML and AI is that ML learns passively from the data it trains on. However, while AI is broader and more expansive and can include ML, AI has other ways of algorithmically mimicking human psychology. This can include knowledge graphs, ontologies — like what is the basis for CulturePulse technology — and smart social systems with complex interactions between different agents and their environment. People use machine learning across many applications, from movie recommendations from a streaming service to what shows up in a social media feed to image and speech recognition and even natural language processing systems.

Machine Learning

The Resonance Score gauges how a message aligns with its intended audience. It is calculated by testing against audience <a href="https://culturepulse.ai/lexicon/digital%20twinshttps://www.culturepulse.ai/lexicon/digital-twin" rel="noopener noreferrer" target="_blank">digital twins</a> that are created with <a href="https://www.culturepulse.ai/lexicon/multi-agent-ai" rel="noopener noreferrer" target="_blank">multi-agent AI</a>. The algorithm that drives the Resonance Score measures 85+ social and psychological dimensions across 40+ languages, allowing users to forecast how well their market or target audience will receive, react to, and resonate with their content. The higher the score, the stronger the message's reach and resonance. Users can safely A/B test against their digital twin and publish when their messaging is perfect. <h2>Resonance Score Metrics</h2>The Resonance Score isn’t scaled from 1 to 100. It’s measured against other creatives in the <a href="https://app.culturepulse.ai/text-analysis/1" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Reddit A/B Testing module</a>. The highest score that a CulturePulse partner agency has ever reached is 26 for a 300-character ad. That agency’s client subsequently put seven figures behind it. As a general rule for 100-character headlines, it’s recommended to publish with a score no lower than 10. It’s easier to generate higher scores with higher character count ads.&nbsp;

Resonance Score

A digital twin is a digital replica of a physical thing or process. The concept of a digital twin dates back to 2002. The first practical definition of a digital twin came from NASA when they tried to improve the physical model simulation of spacecraft in 2010. A digital twin can be digitally created to model something as small as a single machine component to an entire factory, building, or the world or brain. Digital twins can stand alone to see how they fare under a variety of conditions, such as temperature or pressure, but they can also integrate with other tools, like AI. CulturePulse’s social digital twins are typically created as <a href="https://www.culturepulse.ai/lexicon/multi-agent-ai" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">multi-agent AI</a> (MAAI) systems pioneered by <a href="https://www.culturepulse.ai/team" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Justin Lane</a> and <a href="https://www.culturepulse.ai/team" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">F. LeRon Shults</a>. CulturePulse digital twins of social groups are based on psychological profiles generated from text data — of a subreddit, a community on Twitter, or a local population. These digital twins have the same beliefs and values as the individuals and groups you want to connect with. This allows users to safely test messaging until they’re satisfied with their content, their audience’s reaction to it, and their <a href="https://www.culturepulse.ai/lexicon/resonance-score" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Resonance Score</a>. <h2>Digital Twin Projects</h2>While digital twin technology isn't yet commonplace, many companies across diverse industries are using it to improve their understanding of how the world works globally, physically, and locally. <ul><li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcfall22-a41326/?playlistId=playList-9bb5405e-3e40-4ff3-88db-61cd3a4507e5" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">NVIDIA’s Earth -2</a> is a digital twin of the earth to understand weather and climate change.</li><li><a href="https://www.neurotwin.eu/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Neurotwin</a> is an EU-funded project that’s trying to simulate human brains to help predict the most efficacious treatment for conditions like Alzheimer's and epilepsy.</li><li>Shanghai. <a href="https://www.51aes.com/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">51World</a> created a digital twin of Shanghai and all 26M of its people and everything else that makes it what it is.</li></ul> The diversity of digital twin applications is inspiring because they showcase the breadth of scientific, communal, and social understanding and innovation coming to communities all around us — and even improving the quality of our lives.

Digital Twin

Prediction is the process of using data to make informed guesses about future events or outcomes. One way to do this is by training&nbsp;<a href="https://www.culturepulse.ai/lexicon/machine-learning" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(74, 110, 224);">machine learning</a>&nbsp;models on historical datasets to identify patterns and relationships. These patterns and relationships help the machine learning algorithms make predictions about new, unseen data. <a href="https://www.culturepulse.ai/lexicon/agent-based-modeling" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(74, 110, 224);">Agent-based modeling</a>&nbsp;(ABM) can also be used to make predictions based on the agent behaviors and interactions in a virtual&nbsp;<a href="https://www.culturepulse.ai/lexicon/simulation" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(74, 110, 224);">simulation</a>, like a&nbsp;<a href="https://www.culturepulse.ai/lexicon/digital-twin" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(74, 110, 224);">digital twin</a>.&nbsp;<a href="https://www.culturepulse.ai/lexicon/multi-agent-ai" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(74, 110, 224);">Multi-agent AI</a>, a subset of ABM created and pioneered by Justin Lane, CEO of CulturePulse, models behavioral and psychologically realistic agents to leverage predictive outcomes. Predictive outcomes are all around us. For example, a meteorologist might use historical weather data to build a model to predict conditions. Another example is email. Have you ever gotten email promotions for a product you’ve stopped using? Those emails are part of lifecycle marketing campaigns to lure back people like you who’ve churned. That email exists because of learned predictive behavior.

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Resonance

Machine Learning

Resonance Score

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