	US 12,079,817 B2
	Agent awareness modeling for agent-based modeling systems
Theresa Sullivan, Cincinnati, OH (US)
Assigned to Ignite Marketing Analytics, Inc., Austin, TX (US)
Filed by Ignite Marketing Analytics, Inc., Austin, TX (US)
Filed on Jan. 25, 2022, as Appl. No. 17/584,168.
Application 17/584,168 is a continuation of application No. 16/299,414, filed on Mar. 12, 2019, granted, now 11,270,315.
Application 16/299,414 is a continuation of application No. 13/667,217, filed on Nov. 2, 2012, granted, now 10,248,957, issued on Apr. 2, 2019.
Claims priority of provisional application 61/554,565, filed on Nov. 2, 2011.
Prior Publication US 2022/0148020 A1, May 12, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0202 (2023.01)

CPC G06Q 30/00 (2013.01) [G06Q 30/0202 (2013.01)]

18 Claims

1. A system for modeling agent awareness in an agent based model, the system comprising:

one or more processors; and

one or more memories, coupled to the one or more processors, storing code that when executed by the one or more processors causes the one or more processors to perform operations comprising:

assigning a plurality of attributes to each agent of a plurality of defined agents;

for each agent, generating a uniformly-distributed, blended variable value for values of at least two of the attributes in the attribute group that are uniformly-distributed by blending;

for each agent, assigning the generated blended variable value as an additional attribute value;

tracking a ratio of indicators for each agent;

varying the ratio of indicators for each agent upon the occurrence of a triggering event for that agent, based on at least one of the values of the plurality of attributes in the attribute group and the blended variable for that agent;

using the ratio as a factor to model the agent's awareness as a continuous, non-discrete value;

running a series of test simulations in which at least one variable is adjusted by an amount while the remaining variables remain constant;

determining a change in mean absolute percentage error (“MAPE”) of each of the test simulations;

for each of the test simulations, automatically optimizing the values by at least adjusting values of variables that cause the MAPE to change by an amount that exceeds an error for the test simulation to optimize the simulation of the human behavior;

utilizing the values of the variables to control simulation of the human behavior by the agents;

simulating human behavior by the agents including agents making decisions and reacting to input stimuli according to individual characteristics and constraints of each agent;

generating simulated human behavior output data from the simulation agents; and

applying the simulated human behavior output data to determine and implement a real-world plan to exploit expected human behavior based on the simulated human behavior.