TOP 5 Significant Use Cases of Agent Based Modeling Simulation

Every human behavior is based upon decisions that prompt ecological populations to decide various activities. The decision involves what to eat, when to eat, where to move, and whether to travel or not.  

Well, every individual being follows certain rules to regulate their decision making process based on the theory of the population models. However, these models are supposed to be inaccurate to foster real-time decisions on human actions. In addition, certain factors influence the decision making process. They include the environment with its risks and rewards, the complex social parameters, lack of experience and learning about the environment.

Here, enters the agent-based simulation (ABS) or Agent-based modeling (ABM). It involves simulation technique and a model that parses mannerisms of actions and interactions between individuals and the environment in a program. In agent-based simulation model, it refers to the independent decision-making bodies known as agents. As is with decision-making, each agent examines its situations and makes decisions using some rules.

Let’s understand agent-based simulation or Agent-based modeling (ABM).

What is Agent-based Modeling?

It is an effective simulation modeling process to be used in different types of applications including real world business problems.

In agent-based modeling, the whole procedure is orchestrated upon agents that carry out autonomous decision-making known as agents. Agent-based modeling gives out more accurate results about competitive interactions between agents using computational theories that explore dynamics. The scope is limited with the pure mathematical processes.

To have a simple definition about agent-based modeling, it means a collection of autonomous agents and relation between them. Even a simple agent based model can parse complex behavior patterns of consumers. Most often, it can provide valuable information about dynamics and enable unanticipated human behaviors to come up.  A wide range of tools such as neural networks, machine learning techniques, and evolutionary algorithms are used in this application to parse information.

This AI-based model offers a real-time application which is so beneficial for every industry. Its flexible implementation is an underlying reason for its popularity. We will first explore a number of benefits of the technique and they discover its application of use case by various industries.

Benefits Of AI-Based Agent-Based Modeling

There are three core benefits of ABM.

a.    ABM drives emergent phenomena

b.    ABM defines a natural system accurately

c.    ABM is flexible

ABM’s emergent phenomenon is a powerful process that precedes the other two benefits.

·         ABM captures emergent phenomena

The interactions between individual entities result in the emergent phenomena. As the definition goes, the system is not held liable for any causative incidents because of the interactions between the systems or parts. With emergent phenomena, it has decoupled properties not associated with the part. For example, a traffic jam that results from the drivers’ faults, is identified as a different reason not related to vehicles that cause it. Under emergent phenomena, the scenario is tough to discern and predict.

·         ABM Defines A System With A Natural Definition

ABM seems to produce the most probable reality about scenarios involved with traffic jams, stock markets or polling. For example, it is of more use to assume shoppers behavior in a supermarket than describing their density. It also helps companies work with real data about their users retrieved from pane data and customer survey.

·         ABM Offers Accurate Assumptions

ABM uses a flexible model to test strategies and discern a real cause of something that happens or something about to happen and more. Also, it is a key predictive modeling technique used for business analytics.

Based on the benefits it offers, we would find out some key use cases of AI-based Agent –based modeling simulation.

Application areas of Agent-based Modeling Simulation

ABM’s emergent phenomenon has become progressively accepted tool to predict difficult and counterintuitive situations in various moments.

1. Improving Evacuation Resulting From Herding Behavior

Crowd stampede can occur in any place such as temples, shopping malls, movie theatres and many. Generally, it happens due to panic that results in fatalities. Many times, such incidents come about in an overcrowded place for fire breakout or rush for seats. More common disasters are quite frequent during mass gatherings in pop concerts, sporting events and many. When people are self-obsessed during such incidents, they lose control over their actions, leading to irrational herding. As an overall result, it results in overcrowding and blocks easy escape routes. In this situation, Emergent Phenomenon is applicable since it addresses panic behavior of people resulting from   the complex human behavior and individual interactions. Based on the theory extracted from emergent phenomenon, ABM can offer insightful suggestions on mechanisms of panic and jamming. Using neural network and machine learning, Agent-based modeling can parse panic and jamming behavior of herding people, and offer simulation results that can help reduce harmful accidents and suggest optimal escape strategy.

4. Evaluating Market Risks

The stock market is a dynamic place where incessant interactions happen between different agents such as investors, issuers, law makers, and economics and policy makers, resulting in emergent phenomenon. Every stock market tends to new trading policies in compliance with regulatory systems.  This is quite high-risk, which can generate a negative response from market shares, investors, and issuers.

ABM simulation can help understand the market more efficiently, and help gauge the impact on tick-size reduction. The simulation model gauges different factors under many conditions. This enables stock markets regulators to have better understanding of different strategies, observe the market behavior in response to changes, and offer warning prior to the occurrence of unwanted financial consequences faster. The agent-based modeling simulation uses neural network and other artificial intelligence techniques to design a regulatory system to prevent financial damage, and improve consistent performance.

Improving Customer Retention

 It is true that acquiring a new customer costs you five times more than the existing customer. It makes sense to increase loyalty and trustworthiness with your existing customer. Using different strategies for customer retention can help you reach your goal. When it comes to customer retention, it is important to pay attention to the customer churn as well since they are integral to each other. ABM helps develop an improved platform to discover influence on customer churn and offer better strategies for customer retention.

Conclusion

ABM is more than a simulation tool, it helps   reduce operational risk and develop ideas to rebuild the organization strategies. We expect to have a wide application of ABM simulation across various organizations and days are not too far when we can see its routine application in audits too.

For any suggestions on ABM simulations and other AI-based technologies, you can contact SynergyLabs. It is an expert and renowned AI consultant to drive your business and offer you more opportunities beyond your imagination. Feel free to contact us.