Creating Social Agent Systems in Simulated
Physical Environments
Principal Investigator: Dr. Ben Aguirre
Research Assistant: Eric Best
(ericbest@udel.edu)
1

• Most simulations create a number of avatars that are largely homogeneous and do not have linked goals.
• Based on our research, we believe this is an inaccurate way to create models of human beings.
2

• Based on our work at the Disaster Research
Center (DRC), we have found that people evacuating a hazard do not rely on individual goals and behaviors.
• Group behaviors and crowd behaviors impact goals and reasoning.
3

• Historically, the DRC has found that evacuees use social behavior when assessing and reacting to hazards.
• Social bonds are not ignored when dangers are present.
• Individual, social, and crowd behaviors interact together to influence decisions and goals.
4

• Recently, Dr. Ben Aguirre conducted a study that gathered detailed information about the victims and evacuees of The Station Nightclub fire.
• We are aware of detailed exit goals, social behaviors, and networks among victims and evacuees.
5

• Using this detailed dataset, we chose to create a set of models incorporating social behavior to replicate the immediate reaction to the disaster at The Station.
• Eventually, we hope to encourage other model builders to consider group behavior and nonhomogenous agents.
6


Humans do not interact in a vacuum, but most simulations of humans account only for individual behavior.

Agent-Based modeling with learning agents is an obvious avenue for creating simulations with evolving group behavior models.
7

• Model builders are largely ignoring group and crowd behaviors, so we set out to build a model incorporating social behavior models.
• Today, we will discuss some of the conceptual and practical issues that must be addressed when creating models with group behavior attributes.
8


One of several first efforts in physical social agent simulation

Several benchmarks
9

• Goals for model creation:
– Benchmark current models and actual results.
– Analyze data and create an individual behavior model.
– Create models with group behavior attributes.
– Apply framework to other situations.
10

• For The Station, we have a detailed dataset with results that we can compare to output from models of the disaster.
• There are several models that have been created about The Station, many with a different focus, but none with social behavior.
• We have exit data and exit time data. Most results compare flow rates in aggregate.
11

• The DRC conducted a study gathering highly detailed data of victims and evacuees, including social bonds to other participants.
• Using this data, we were able to approximate the likelihood of behavior changes based on different social bonds.
12

• Like many others, we created a model with individual behavior.
• This became our personal benchmark. If we can control all of the parameters, we can see the exact impacts of adding group behavior traits.
• Our individual behavior model has similar flow rates to data results and prior models.
13

• We began with an issue of geography. We had to create an accurate environment of The
Station. For now in two dimensions.
• We then added hazards, reasons to leave the building.
• We then created our avatars, or agents.
14

• Our avatars have different behaviors based off of observed traits, such as age, gender, familiarity with the environment, and other factors. They are not homogenous.
• Every avatar has a large set of constraints, such as the inability to move through walls.
• Avatars also have to navigate in the changing environment due to other avatars moving.
15

• Once our individual behavior model was running, we added other layers:
• Individual layer.
• Social group layer.
• Crowd layer.
16

• After creating an individual behavior model, we began to add group elements.
• Almost immediately, our efficiency plummeted, avatars stopped exiting, and our results made little sense when examined by a human.
17

• It is easy to forget that computers are not nearly as smart as you are.
• Every time you add a variable to an agent's decision tree, you have to account for interactions.
• Conflicting goals will render a machine's reasoning system useless unless they are programmed to perform an action or prefer a result.
18

• The moment the actions of other avatars matter to an agent, a high number of interaction variables emerge.
• A simple first-order interaction of 500 agents creates 125,250 unique relationships.
• Adding additional complications can increase interactions exponentially.
19


Before you can create rules or preferences, you have to identify the issues.

The simplest interactions can be hard to quantify.
20

• There is a multi-step process of conceptualization, implementation, and robustness testing.
• This process is inherently interdisciplinary.
21


These physical issues quickly become difficult computational and judgement problems.

Simple group movements require constant recalculation
22

• Individual, group, and crowd behaviors create large numbers of scheduling issues and interactions that must be understood.
23

• Reasoning that humans take for granted is often difficult to implement in computerized systems.
• It is not feasible to truly simulate humans, so it becomes important to focus on a few programmable traits.
• How are social traits programmable?
24

• Here is an example meant to relay the concept of object permanence.
• How do you teach this to a computer, or even communicate it to a programmer?
25

• Identifying and coding behaviors merges ideas and techniques from sociology, psychology, computer science, economics, and statistics among others.
• The DRC is uniquely positioned to accept these challenges thanks to strong interdisciplinary programs and expertise sharing.
26


Agents that can interact together are a natural fit for physical environment simulations. In theory, they just require one more layer of rules to create a physical reality.

In practice, group behavior interactions and physical limitations are going to keep a young field busy for quite awhile. Humans are more difficult to simulate than one might imagine.
27

• Hurdles:
– High-Performance Computing: Relationships tax computing resources. Thankfully, we now have parallel systems and GPU computing.
– Peer Review: Designers are reluctant to share code, and even if shared, it is often difficult to understand. Debugging is very time intensive.
– What attributes to code: Who decides what aspects of group behavior are important?
28

• Currently, I spend most of my time tackling the third issue; which traits are most important to include.
• To begin, I created a version of our model with a simple leader/follower group model.
• These groups are small social dictatorships, where followers pattern a leader exhibiting an individual behavior model.
29

• Our eventual goal is to implement distributed social behavior models, approximating complex social relationships.
• These models will have decentralized and robust leadership, much like networked computer systems.
• Beyond groups, we will incorporate crowd models.
30