# Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the
Lens of Hobbesian Social Contract Theory

Gordon Dai<EMAIL_ADDRESS>New York UniveristyNew YorkNew YorkUSA ,  Weijia
Zhang<EMAIL_ADDRESS>University of illinois at Urbana-
ChampaignUrnanallinoisUSA ,  Jinhan Li<EMAIL_ADDRESS>New York UniversityNew
YorkNew YorkUSA ,  Siqi Yang<EMAIL_ADDRESS>University of illinois at
Urbana-ChampaignUrnanallinoisUSA ,  Chidera Onochie lbe<EMAIL_ADDRESS>University of illinois at Urbana-ChampaignUrnanallinoisUSA ,  Srihas Rao
<EMAIL_ADDRESS>University of illinois at Urbana-ChampaignUrnanallinoisUSA
,  Arthur Caetano<EMAIL_ADDRESS>University of California, Santa
BarbaraSanta BarbaraCaliforniaUSA and  Misha Sra<EMAIL_ADDRESS>University of
California, Santa BarbaraSanta BarbaraCaliforniaUSA

(2024)

###### Abstract.

The emergence of Large Language Models (LLMs) and advancements in Artificial
Intelligence (AI) offer an opportunity for computational social science
research at scale. Building upon prior explorations of LLM agent design, our
work introduces a simulated agent society where complex social relationships
dynamically form and evolve over time. Agents are imbued with psychological
drives and placed in a sandbox survival environment (visualized in figure 1).
We conduct an evaluation of the agent society through the lens of Thomas
Hobbes’s seminal Social Contract Theory (SCT). We analyze whether, as the
theory postulates, agents seek to escape a brutish ”state of nature” by
surrendering rights to an absolute sovereign in exchange for order and
security. Our experiments unveil an alignment: Initially, agents engage in
unrestrained conflict, mirroring Hobbes’s depiction of the state of nature.
However, as the simulation progresses, social contracts emerge, leading to the
authorization of an absolute sovereign and the establishment of a peaceful
commonwealth founded on mutual cooperation. This congruence between our LLM
agent society’s evolutionary trajectory and Hobbes’s theoretical account
indicates LLMs’ capability to model intricate social dynamics and potentially
replicate forces that shape human societies. By enabling such insights into
group behavior and emergent societal phenomena, LLM-driven multi-agent
simulations, while unable to simulate all the nuances of human behavior, may
hold potential for advancing our understanding of social structures, group
dynamics, and complex human systems.

††copyright: none††journalyear: 2024††conference: ; ; ††booktitle:

Figure 1. The image visualizes the simulation environment in which our LLM
agents operate. Two types of resources (food and land) are presented. Agents
make the choice between farming (generating food with their work), trading
(exchanging resources), or entering into conflict with other agents (with the
goal of acquiring more resources) every day. Their primary motivation is
survival.

This is a picture depicting a environment where the agents live. They are
performing different actions, including robbery, exchanging resources and
farming.

##  1\. Introduction

Simulating human behavior that is reflective of real world behaviors under
similar circumstances is of value across multiple domains from social
psychology to conflict resolution and behavioral economics. Over the last
several decades, researchers and practitioners have created computational
agents in video games (Riedl and Bulitko, 2012), modeled behaviors (Gratch et
al., 2007) and studied them in virtual environments (see survey (Kyrlitsias
and Michael-Grigoriou, 2022)). The vision is for agents to not only make
interactive gaming more realistic, but to employ them in training (Rickel,
2001; Hollan et al., 1984; Tambe et al., 1995), testing (Binz and Schulz,
2023), embedding in user interfaces (Cassell, 2001; Maes, 1995; Koda and Maes,
1996), and robotics (Bates et al., 1994; Ruhland et al., 2015) where they act
consistently based on their past experiences and respond appropriately to an
ongoing situation. Recent work on the design of computational agents based on
large language models (LLMs) by Park et al. (Park et al., 2023) has proposed
an “agent architecture that stores, synthesizes, and applies relevant memories
to generate believable behavior.” With the proposed architecture, an
interactive game environment with 25 agents was designed to demonstrate the
difference between manual scripting of a game event and multiple characters
involved vs achieving a similar outcome by informing one LLM agent about their
intention to host an event with five other agents arriving to participate in
the event. The informed agent shared information with other agents through
LLM-supported conversations, showcasing the use of LLMs and the proposed agent
architecture in generating emergent behavior in the example gaming scenario.

Another recent work has simulated the decisions and consequences of countries
involved in World War I, World War II, and the Warring States Period in
Ancient China (Hua et al., 2023). This multi-agent AI system showcases how we
may re-create complex situations, run dynamic what-if scenarios, and use
computational modeling for foreign policy decisions in the future. In this
particular case, each LLM agent is a country. The agents are programmed to
mimic the behaviors, strategies, and decision-making patterns of the countries
or leaders they represent. The LLM provides the underlying intelligence for
these agents, enabling them to act in historically plausible ways. The LLM-
based agents interact with each other in the simulation environment based on
historical alliances, conflicts, economic conditions, and other relevant
factors. LLMs can generate dynamic scenarios by altering key variables or
introducing new elements. For example, changing diplomatic alliances, economic
conditions, or military strategies can create different outcomes, allowing
researchers to explore “what-if” scenarios.

Building upon previous explorations of LLM agent-based simulations, our work
explores a multi-agent sandbox simulation where LLM agents, imbued with
traits, motivations, and access to scarce environmental resources, are
governed primarily by survival instincts. In contrast to prior efforts
modeling agents as entire nations or individuals in small towns, our agents
exist in a resource-constrained realm where their fundamental drive for
survival necessitates interactions with other agents, to form and update
social relationships. Agent behaviors oscillate between cooperation and
competition, enabling social evolution to unfold organically.

By instantiating the agents with evolvable strategies and motivations loosely
inspired by Evolutionary Game Theory (EGT) (Weibull, 1997), we conduct a
”what-if” inquiry: what societal dynamics arise when self-interested agents
face existential resource pressures? EGT provides a theoretical grounding for
our simulated scenario, including individual incentives, resource scarcity,
and adaptive behaviors like cooperation or competition. Our agents’ prime
directive to secure limited resources spurs the employment of strategic
resource acquisition behaviors and frames the resulting dynamics of
cooperation versus conflict. Uniquely, our framework captures this
foundational societal crucible at the resolution of individual agents,
offering a new computational lens into the microcosmic forces that forge the
intricate fabric of societies.

When given self-preservation instincts, our LLM agents initially engage in
competition, resorting to zero-sum robberies to secure resources. However, as
the simulation progresses, a transition emerges and agents gradually learn to
cooperate. They form strategic alliances, peacefully exchanging resources and
expecting protection against robberies. This behavioral adaptability, governed
by the experience through interactions with other agents, demonstrates an
intrinsic capability to gravitate towards mutually beneficial arrangements.
Analyzing this simulated societal evolution through the lens of Thomas
Hobbes’s Social Contract Theory (SCT) (Riley, 1982), we find a parallel. The
artificial agent society progresses from the beginning where individual self-
interest is predominant, to the establishment of a cooperative soceity
centered around a single agent predicated on the surrender of certain freedoms
in exchange for shared security and stability, mirroring the theoretical
trajectory outlined by Hobbes for the emergence of human societies.

To investigate the factors influencing the emergent societal dynamics, we
conducted experiments by systematically manipulating agent and environment
parameters. Our findings reveal a direct correlation between these variables
and the resultant social outcomes, indicating agent behaviors were indeed
shaped by their innate drives (as represented by the parameters) and
experiential feedback. Crucially, we also address the robustness of our
simulation framework by exploring how variations in key independent variables
impact the experimental outcomes. These variables include agent-level
parameters like memory duration, population scales, and the precise phrasing
of prompts that shape agent motivations. By comparing outcomes across
permutations of these variables, we gain insights into the potential
sensitivities and boundary conditions that could arise from alternative
simulation settings. This rigorous approach enhances confidence in the broader
applicability of the societal phenomena observed while highlighting the
underlying dynamics.

Our contributions are as follows:

  * •

A novel multi-agent simulation framework that yields believable artificial
societies capable of dynamically replicating complex human group behaviors and
social interactions. These emergent social dynamics are conditioned by the
interplay between agents’ innate psychological drives, their intrinsic
motivations, and the constraints of their simulated environment.

  * •

Empirical evidence, through systematic experiments, establishing correlations
between agent attributes (e.g., memory, incentives) and available resources on
one hand, and the evolutionary trajectories of simulated societies on the
other. These findings highlight the fundamental factors underpinning social
emergence and change in the simulation.

  * •

A discussion analyzing the collective behaviors of the generative agents,
highlighting the opportunities and potential risks associated with leveraging
LLMs for societal simulations with potential impact for social science
research.

  * •

An extensible social simulation platform that empowers researchers to
operationalize a wide range of social science hypotheses through customizable
scenario configurations, enabling exploration into the roots of group
dynamics, societal organization, and the forces that shape the human
experience.

##  2\. Related Work

###  2.1. Traditional Computer-Based Social Simulations

Prior work in computer simulation research on social behavior has explored two
classical types of models, namely Thomas Schelling’s neighborhood segregation
model in 1971 (Schelling, 1971) and Robert Axelrod’s Reiterated Prisoner’s
Dilemma (RPD) simulations on the evolution of cooperation in 1984 (Barker,
2021). Schelling’s model was designed to investigate the role of choice in
bringing about the segregation of either predominantly black or white
neighborhoods. By assuming that individuals tend to move to empty regions when
not surrounded by enough neighbors of their own group, the model showed that
segregated neighborhoods tend to emerge from a randomly distributed
neighborhood. Surprisingly, initially integrated neighborhoods also ended up
fully segregated. This simulation highlighted the role of micro-level
behaviors in driving macro-level social phenomena and has been influential in
understanding various forms of racial, ethnic, and economic segregation. In
Axelord’s RPD studies, the researchers put Prisoner’s Dilemma, a classic
scenario in game theory, into a computer simulation. For the two public-based
tournaments involving computer programs playing the Prisoner’s Dilemma game,
the host Axelrod, found that ”Tit for Tat,” a strategy that begins by
cooperating and then mimics the opponent’s last move in subsequent rounds, was
resilient and effective in fostering cooperation.

Eckhart Arnold (Arnold, 2014) argued that for formal models such as
simulations to be applicable to social processes, two important prerequisites
of robustness, and empirical identifiability, should be satisfied. Under this
standard, as Arnold argued, Schelling’s simulation is applicable whereas
Axelrod’s simulation is not, given that variations of the RPD model can be
constructed where the conclusion becomes invalid. Generalizing from these two
classical examples, Arnold proposes four dangers associated with mathematical
and computational modeling, including (1) risks of underestimating
unformulable causal links, (2) risks of arbitrary decisions due to limited
empirical insight, (3) risks of false understanding from flawed models, and
(4) influence of modeling on question selection over societal importance.
However, scholars have advocated for simulations as valuable tools to model
and explore social phenomena that are otherwise challenging to test in the
real world (Mayo-Wilson and Zollman, 2020).

###  2.2. LLM-Based Simulations

The popularization of generative AI and LLMs has attracted scholars from
various fields exploring interdisciplinary applications, including social
sciences and humanities. Social scientist Christopher A. Bail (Bail, 2023)
explored the potential impact of Generative AI on social science research,
examining how it could enhance methodologies including online experiments,
agent-based models, and automated content analysis. By fine-tuning ChatGPT-3
with works of contemporary philosopher Daniel C. Dennett, Schwitzgebel et al.
(Schwitzgebel et al., 2023) compared the text produced by Dennett and his LLM,
finding that the LLM can convincingly emulate philosophical discourse for non-
expert readers. LLMs have also been used to create ”Living Memories” of
historical figures such as Leonardo Da Vinci or Murasaki Shikibu
(Pataranutaporn et al., 2023). These are interactive digital mementos created
from journals or letters an individual has left behind to make their
knowledge, attitudes and experiences more accessible to people in a
conversational format. Our work, on the other hand, shifts the focus from
emulating individual agents to exploring emergent group dynamics.

###  2.3. Interactive LLMs

Interactive AI systems aim to combine human insights and capabilities with
computational systems that can augment human performance. In his essay, J.C.R.
Licklider referred to this kind of human-AI system as “man-computer symbiosis”
(Licklider, 1960). Douglas Engelbart offered a theoretical framework for
“augmenting human intellect” which also depends on humans and computers
working together (Engelbart, 2021). While both emphasize the idea of
harnessing the power of computing (or AI) to enhance human capabilities,
Engelbart’s framework differentiates itself with a focus on interaction with
technology in a more intuitive manner. Crayons demonstrated an early vision of
interactive machine learning, allowing non-expert users to train classifiers
(Fails and Olsen Jr, 2003), broadening access to a technology, that otherwise
was limited to expert users. Real-time interactive AI systems have numerous
potential applications, including virtual assistants, chatbots, and
educational tools (Lee, 2023). They can also be used in gaming (Ranjitha et
al., 2019), and healthcare (Martinengo et al., 2023), providing immediate and
personalized responses for non-experts. and other areas where immediate and
personalized responses are crucial and users are often not AI experts.
However, the development of real-time interactive AI systems involves
addressing multiple challenges, including natural language understanding,
recognition and interpretation of human emotions, facial expressions or body
language, design of user interactions, and adaptation to individual
preferences, tasks and contexts. As these systems continue to evolve, they are
expected to play an increasingly significant role in enhancing end user
experiences and transforming the way we interact with technology. Zheng et al.
(Zheng et al., 2023) have offered design principles for individuals without
specialized backgrounds in AI to customize their own LLM agents for domain
tasks. Another use case that has been explored is the use of artificial agents
as confederates for humans in social science experiments (Krafft et al.,
2017), which would allow social scientists to manipulate situations easily and
study social phenomenon more effectively, though not without some ethical
challenges. Another team of researchers developed CommunityBots (Jiang et al.,
2023), a platform that utilizes a diverse assortment of chatbots capable of
seamlessly switching between contexts when interacting with humans. This
adaptability led to increased engagement levels, demonstrating the potential
of CommunityBots for automating information elicitation processes typically
performed by humans. Our simulation offers an interactive user interface that
allows end users to manipulate agent parameters as a way to explore their
impact on the outcomes to help experiment and learn about the limitations and
the capabilities of both the LLM-based agent and the simulation design. This
could help researchers determine if and how the simulation might be of use for
their own work before expending considerable resources.

###  2.4. Group Dynamics

As LLM agents get increasingly used in supporting various tasks, it is
worthwhile to investigate their design and behavior when given agency. For
example, LLMs can exhibit human-like characteristics, particularly when they
are designed with detailed personas and without Chain-of-Thought (CoT)
reasoning (Bai et al., 2023). This approach enables the design of agent group
dynamics, potentially leading to a closer alignment with human behavior and
therefore providing greater benefits for the study of human behavior through
LLM-based simulations. The detailed personas can help the agents to assume
more nuanced attitudes and provide individualized responses, reflecting human-
like empathetic psychology, under specific conditions. A prior work explores
how context-aware dialogues generated by LLMs can enhance player engagement,
suggesting a level of empathy and adaptability in interactions (Csepregi,
2021). Similarly, Chuang et al. (Chuang et al., 2023) show that LLMs can
simulate human group dynamics, particularly in politically charged contexts,
by role-playing as different personas. Azad and Mertens (Azad and Martens,
2021) present a taxonomy for classifying social behaviors, inter-agent
communication, knowledge flow, and relationship changes of simulated
characters in virtual environments, to allow researchers to systematically
study agent interactions. Our work also explores emergent group dynamics when
agents are motivated to survive with limited access to resources.

##  3\. Generative Agent Design and Behavior

We instantiate agents as characters in a sandbox world where survival in the
resource constrained environment is their primary motivation.

###  3.1. Setting

The agents are in a natural world where food and arable land serve as the
foundational elements crucial for survival. This setting can be seen as
aligning with the physiological needs outlined at the base of Maslow’s
Hierarchy of Needs (Maslow, 1943). In our pre-social world with limited
information transparency, each agent knows the existence of other agents, but
nothing more. In the baseline setting, there are 9 agents, each initially
possesses 2 units of food and 10 units of land, a natural state of scarcity.

Figure 2. Our interactive user interface; the left-hand side displays the
attributes (aggressiveness, strength, etc.) of Agent 2, current resources
(food and land), relationships with other agents and information about their
current and pending actions, and memory; the right-hand side shows the
simulation log with each action documented as an emoji.

This is a picture of our user interface. On the top left, it shows the Agent
2’s personal attributes (aggressiveness, strength, etc.), current resources
(food and land), relationships with other agents and information about their
current action of the day. On the bottom left, it shows the agent’s pending
action in the current round, agents that concede Agent 2, rob interactions
with other agents, and Agent 2’s memory. On the right is an overview of the
whole simulation. Each action performed by every agent is documented by an
emoji in the top right. The system log is visible the bottom.

###  3.2. Agents

Agents make choices of action based on their psychological traits and memory.

####  Traits:

We prompt agents with an approach that combines quantified parameters with
non-quantified textual descriptions. We set independent attributes
(self.attributes) to partially emulate agent’s psychology including:

  * •

Aggressiveness, sampled from $\mathcal{N}(0,1)$, the degree of an agent’s
tendency to engage in violent behaviorws;

  * •

Covetousness, sampled from $\mathcal{N}(1.25,5)$, the degree of an agent’s
desire for more assets that are beyond necessity;

  * •

Strength, sampled from $\mathcal{N}(0.2,0.7)$, related to an agent’s winning
rate in violent behaviors;

We also set a constant for the desire of peace to provide a basis for agents
to evaluate their conflicting desires. For textual-psychological descriptions,
we took reference from evolutionary psychology (Buss, 2015) and constructed a
prompt that describes the need for survival and for pleasure, on top of which
there is a desire for peace and stability which stems from long term survival,
and ultimately, a hope for social status as a path to reproduction and social
support, all under the framework of self-interest.

####  Memory:

Each agent remembers the most recent 30 actions they are involved in, either
as the recipient or the initiator of an action, saved as a text log. Memories
allow each individual to learn from their previous experience, based on which
different choices could potentially be made in the future. At the beginning of
the simulation there is no memory, nor are there any social relations, so each
individual knows very little about other agents apart from the fact that they
exist. Very limited information about others, or unfamiliarity, in theory,
does not allow for trust. As the simulation progresses and memories
accumulate, we expect individuals to begin to understand their comparative
strengths and weaknesses, and adjust their survival strategies accordingly.
For example, regular winners of confrontations could feel emboldened to rob
more and those who lose often may want to concede more in order to be
protected from their stronger neighbors, thereby, leading to emergent social
contracts.

#### Context:

Along with memory and psychological traits, we also prompt the agents with a
constant description of the world they live in and the significance of each
action.

###  3.3. Actions

There are four actions that each individual can initiate each day. Farm and
donate are unilateral actions, whereas trade and robbery are interpersonal
actions. Each individual has to respond as many times as needed.

####  Farm:

enables agents to obtain their food from the land they have, which represents
their self-preservation. If all agents choose to farm, then they stay at pre-
social autonomy. The food A receives is determined by the following:
$\text{food}=\text{land}\times\mathcal{U}(0,1)$. This action is the most
fundamental action where individuals do not interact with others.

####  Rob:

is when one agent attempts to take another agent’s food or land without
offering their own food or land in exchange. It represents a zero-sum
interaction with conflict, in which the gain of one agent means the loss of
another. This action symbolizes the concept of competition in evolutionary
psychology, since resources are limited compared to desires (Barrett et al.,
2002). When someone is being robbed by another, they can choose to either
resist or concede.

  * •

Resist signifies an agent’s attempt to defend their food or land against the
robber. The probability of agent A winning over agent B, is defined by the
sigmoid ($\sigma$) function:

(1) $P(A.\text{win})=\sigma(A.\text{strength}-B.\text{strength})$

The winner of a confrontation not only gains food or land but also gains
social status; the loser loses social status along with their food or land. In
the decision-making process, each agent is prompted to evaluate their
inclination towards resistance, which is determined by a combination of their
win rate and desire for glory, and contrasts it with their desire for peace,
which is associated with conceding. Agents ultimately select and execute the
response that yields the highest utility based on this evaluation.

  * •

Concede signifies the creation of a contract where an agent allows a robber
agent to take either food or land from them arbitrarily, and as an exchange,
the robber is expected to protect that agent against future robberies, which
could be a mutually beneficial setup. We specifically prompt agents to be
mindful about conceding since it is a permanent contract, and to only make
such a contract when they are absolutely sure that they will rarely win in a
confrontations.

####  Trade:

stands for one agent acquiring another agent’s food or land by offering their
own food or land as an exchange. When one agent receives a trade offer from
another agent, that agent can either choose to “accept” or “reject” the offer.
This action symbolizes cooperation, a critical aspect in evolutionary
psychology where humans are forced to work with each other to survive during
pre-historical times (Zhao, 2021).

####  Donation:

is made when one agent voluntarily assigns their own food or land to another
agent, which represents peaceful, altruistic behavior that is not expected to
happen at all based on the self-centered psychology the agents are prompted
with.

###  3.4. Output Data and User Interface

The output log contains a chronological record of all the activities that take
place on each day and round of the simulation. A CSV file records the
statistics of farm, trade, robbery, and concession between individuals. Since
no donation happened in all our trials, it was not included it in the output
data. This data enabled us to evaluate how the agents evolved and results are
presented in Section 5.

We designed an interface (Figure 2) to display the simulation logs as well as
the real-time status of each agent, their personal attributes, possessions,
and their social role (free agent, subordinate, or superior) as it evolved.
Pending action refers to the rob or trade action an agent needs to respond to.
Memory stores the most recent 30 events. The right side log contains the full
response that the LLM generates and the reason for doing that action. For
example, in the figure, Agent 1 plans to rob Agent 3 because it wants to
increase the land it owns and thereby its social position. A brief summary is
given in the bottom window, where the emojis of sword, rice and handshake
represent the actions that each agent makes.

Figure 3. The flowchart shows the flow of the simulation in a ”day” where each
agent takes turns to perform actions and respond to actions performed by other
agents.

This is a flowchart demonstrating the flow of the simulation in a day. Each
agent take turns to perform actions. They respond to pending actions such as
rob or trade first, then they perform their initiative action if they haven’t
done so in the day. Possible reactions for trade include agree and reject.
Possible reactions for rob are resistance or concession. Conceding to another
means accepting all of their future rob actions without resistance.

##  4\. Simulation

Before the simulation starts, agents are created and instantiated for their
personal traits according to Section 3.2. Each agent has a unique index number
for referencing purposes. “Intelligence” of each agent is defaulted to be 1
for every trial, except the ones where we set intelligence as an independent
variable (Section 6.2) that we manipulate to explore impact on the outcome.
All these variables are fixed and do not change over the duration of the
simulation.

Aside from these attributes, there are two main types of assets: “land” and
“food.” Every agent begins with 10 units of land and 2 units of food. Time in
our simulation is a discrete concept and its unit is ”day.” Each day, every
agent consumes 1 unit of food, thus, they are incentivized to obtain more
food. Land is an asset that could be used to produce food through the farm
action as detailed in Section 3.3 which also specifies how other actions (rob
and trade) could affect an individual’s assets.

On each day, every agent has an opportunity to perform one of four actions
(farm, rob, trade, donate). The LLM responds with the action name that a
particular agent decides to take. It also specifies the relevant details
related to that action. Below is an example of a trading action. The flow of
the simulation on one day is shown in Figure 3.

> ACTIVE STATE
>
> Action: {
>
> "action": "trade",
>
> "payload": {
>
> "TradePayload": {
>
> "TargetId": 1,
>
> "PayType": "land",
>
> "PayAmount": 1,
>
> "GainType": "food",
>
> "GainAmount": 1
>
> }
>
> },"reason": "I want to trade 1 unit of land with agent 1 for 1 unit of food
> to increase my food supply."
>
> }

In the above example, Agent 0 initiates an interaction with Agent 1 by
offering a specific resource (1 unit of land) in exchange for another resource
(1 unit of food). This action represents Agent 0’s attempt to engage in trade
with Agent 1, fostering a potentially mutually beneficial exchange between the
two agents, depending on Agent 1’s response. Each agent also needs to respond
to every action that they are the target of. For example, Agent 1 responds to
the trade action by Agent 0 by rejecting it. As a result, no resources are
exchanged.

> Agent 1 is responding...
>
> Response action:
>  trade, owner: 0,
>  target: 1,
>  payType: land,
>  payAmount: 1,
>  gainType: food,
>  gainAmount: 1
>
>
> PASSIVE STATE
>
> Agent 1 chooses to REJECT
>
> Individual current action:be traded
>
> Result:{
>
> "result": "Agent 1 chooses to reject the trade initiated by agent 0.",
>
> "is resolved": true,
>
> "new relation": (False, -1)
>
> }

As shown above, when an agent is the target of an action initiated by another
agent, a possible response could be rejecting the action. This response
demonstrates how the targeted agent reacts to the action and showcases a
complete to-from interaction between the two agents, which results in either
cooperative or competitive behavior.

Each agent begins their day by responding to actions by other agents that
target it followed by initiating its own action that targets another agent.
Once there are no actions to respond to and everyone has initiated one action,
a day ends. As the simulation progresses, agent memory accumulates and they
adjust their behaviors accordingly. Here is an example of an agent’s memory
after three days.

> Day 0. I initiated a trade to agent 1, which is to exchange 2.0 units of my
> land for 4.0 units of its food.
> But it rejected it so I gained nothing and exhausted my action opportunity
> for today.
>
>
> Day 1. I tried to rob agent 4, who resisted and l lost. I did not gain
> anything and my social position dropped 1 unit.
>
>
> Day 2. I farmed and gained 2.81132538909987 units of food.

###  4.1. Decision Making

In our simulation, there are many circumstances where agents have to make
decisions between several options. There are two main types of decisions.
Firstly, agents have to choose an initiative action of the day (farm, trade,
rob, donate). Aside from that, they have to decide how to react to actions
that target them, such as whether to agree or not on a trade proposed by other
agents. Whenever a decision has to be made, we request a response from
gpt-3.5-turbo model (hereon referred to as GPT or LLM) using the OpenAI API
111https://openai.com/blog/openai-api. For every request, a general
description of the world and the agent’s circumstances is included in the
prompt. This description is included regardless of the type of decisions that
agents have to make, as it reflects the fundamental world setting and rules of
behavior every agent follows. Template of the general request made to the LLM
is included in the appendix A. (Actual variable names in {} are replaced by
variable descriptions for clearer presentation)

In addition to the general description, if the decision is about the action of
the day, the following prompt is included. The prompt in appendix B explains
the options for the initiated actions and their implications. It also
specifies the formatting details for the LLM’s response. This and the general
description constitutes the whole prompt for requesting the LLM’s decision
about the initiated action.

If the agent is making a decision about responding to another agent’s actions,
the request to GPT includes the general description and specific prompt
regarding this situation. Depending on the type of the action that agent is
responding to, this explains the options of the agents and related
implications. For example, appendix C is the prompt for encountering
competitive behavior (being robbed).

During the experiments, some slight modifications are made when the
relationship between two individuals in the same action is different, such as
superiors and subordinates. These slight variations help us to enforce the
rules of the simulation, such as subordinates cannot refuse the rob action
from superiors.

Figure 4. Transforming from a State of Nature to a Commonwealth

This is a graphical representation showing the transition from the state of
nature to common wealth. Images of farming and conflict are shown under the
state of nature. Images of hand shaking are shown under forming contract.
Images of farming and trading are shown under common wealth

##  5\. Hobbesian Social Contract Theory

To establish a baseline for agent behavior in our simulation, we performed
four identical runs or trials without altering any agent or environmental
parameters. Analysis of the outcomes from these four runs consistently
indicated that all agents eventually yielded to the authority of a single,
common agent. This aligns well with Thomas Hobbes’s Social Contract Theory,
which argues that individuals under the “state of nature” are incentivized to
leave this stage to resolve their internal psychological conflicts (Williams,
2023). The “state of nature” refers to the state of the world before any
social or political order has been established. According to Hobbes, the
natural state of humanity is characterized by perpetual conflict and violence,
a condition famously described as a “war of all against all” (Hobbes, fied).

Hobbes contends that the transition to a commonwealth, where individuals
collectively delegate most of their authority to a central entity by
“authorization,” serves to ameliorate society’s war-like condition. In doing
so, the transition extricates individuals from the state of perpetual
conflict, allowing them to engage in peaceful interactions with one another
(Hobbes, fied). In our simulation, we define commonwealth to be the situation
of a dominant agent with the compliance of others. Figure 4 offers a
simplified illustration of our simulation’s emergent evolution which aligns
with SCT. Applying the Hobbesian perspective, we observe that our agents
transition from a state of nature to a commonwealth by progressively
establishing concessionary relationships. Over time, this dynamic process
leads to the formation of individual social relationships among agents.
Ultimately, as all agents recognize a single agent as their sovereign, the
agent society undergoes a transformation, resulting in the establishment of a
unified commonwealth.

We further explore whether agents meet three essential benchmarks indicative
of evolved behavior as expected within the framework of SCT.

  1. B1:

Do agents begin in a state of nature with numerous conflicts and distrust at
the start of the simulation;

  2. B2:

Are agents able to form contracts and transition to a commonwealth; and

  3. B3:

Are agents able to have considerably more peaceful interactions and less
violence under the commonwealth than in the state of nature.

These benchmarks help differentiate between the ”state of nature” and the
”commonwealth” by highlighting distinct characteristics observed in agent
interactions. Within an experimental setting, if all three benchmarks listed
above are observed, we can suggest that our LLM agents display behaviors
reminiscent of humans, under the framework of Social Contract Theory (SCT) and
evolutionary psychology. This observation suggests that LLM agents may possess
the capacity to emulate certain aspects of human social behavior within these
frameworks.

###  5.1. Baseline Outcome Analysis

Agent parameters were designed within our framework to reflect typical
characteristics found within human populations. In our baseline experiment,
all four runs of the simulation successfully transitioned to a commonwealth,
demonstrating an evolutionary shift towards prioritizing “safety and
security.” We measured this emergence by observing the development of
concessionary relationships among agents, culminating in the point at which
all individuals yielded to a single agent, signifying the onset of the
commonwealth. Table 1 summarizes the main variables and their corresponding
statistical calculations for both the state of nature and the commonwealth
stages within a single trial. Figure 6 illustrates the ratios of variables in
both states. To ensure repeatability of the transition under similar
conditions and rule out the possibility of random chance, we performed four
distinct trials/runs of our simulation. Since we observe the same outcome in
all four runs, we believe our approach helps to validate the reliability and
stability of the observed transition process within our simulation
environment.

Total Count |
---|---
Robbery |  $\sum_{i=0}^{N}$ Robbery initiated by $i$
Resisted Robbery |  $\sum_{i=0}^{N}$ Robbery initiated by $i$ and
| rebelled by $i$’s target
Trade |  $\sum_{i=0}^{N}$ Trade initiated by $i$
Accepted Trade |  $\sum_{i=0}^{N}$ Trade initiated by $i$ and
| accepted by $i$’s target
Farm |  $\sum_{i=0}^{N}$ Farm initiated by $i$
Activity | Robbery + Trade + Farm
Rate |
Robbery | Robbery / Activity
Violence | Resisted Trade / Activity
Trade | Trade / Activity
Accepted Trade | Accepted Trade / Activity
Farm | Farm / Activity
Table 1. Counts and Ratios

Initially, we note significant fluctuations in agent actions, with the robbery
ratio consistently staying above 0.6 and trade and farming around 0.3, as
shown in Figure 5. This result aligns with the first benchmark as listed
above. As the simulation trial progressed, the establishment of concessionary
relationships led to a decrease in robbery and an increase in farming. By Day
21, the society transitioned fully into a commonwealth, with all agents
authorizing a single sovereign agent for order and protection, aligning with
the second benchmark. At this stage, the desire for safety is largely
satisfied. The commonwealth phase showed a steady increase in trade and
farming and a decrease in robbery, indicating peaceful interactions and
meeting the third benchmark. Notably, no agent chose to donate on any day.
Comparative changes in behavior are presented in Figure 6.

Figure 5. Change in Ratios of Robbery, Trade, and Farm wrt Time; a
commonwealth is formed on Day 21 in this trial/run.

This is a line chart showing the rob rate, trade rate and farm rate of the
whole simulation. Rob rate generally decreases, farm rate decreases rapidly in
the first few days but increases in the majority of the time. Trade rate
rapidly increases in the first few days and remains the same in the majority
of the time.

Figure 6. Agent behavior before (in black) and after (in grey) the
commonwealth forms.

A bar chart showing the comparison of statistics before and after
commmonwealth formation. Robbery, violence are more common before common
wealth. Trade and farm are more common after common wealth.

We recognize that Hobbes’s SCT examines complex and intricately diverse human
societies. An LLM agent-based simulation cannot fully replicate the nuanced
behaviors and rich diversity inherent in human populations. Nevertheless, by
observing and analyzing the emergent behaviors of agents within these
simulations, we can gain valuable insights into certain aspects of human
behavior and social dynamics that may help inform our understanding of complex
social systems.

##  6\. Experiments

Going beyond the four runs that constitute the baseline performance of our
simulation (Section 5), we ran further experiments that aimed to explore the
impact of altering agent and environmental parameters on the simulation
outcome. For each modified parameter, we performed three separate runs to
ensure the consistency and reliability of the observed results.

###  6.1. Criteria

We assess emergent phenomena, such as the shift from a state of nature to a
commonwealth, by examining the concessionary relationships between agents. The
initiation of a commonwealth transition is identified when agents start
forming superior-subordinate relationships. Once we observe a point in time
where all agents concede to a single agent, we designate this moment as the
inception of a commonwealth and recognize that singular agent as the central
authority. To evaluate changes in peacefulness among individuals during the
transition from a state of nature to a commonwealth, we compare the ratios of
farming, robbery, and trade actions to the total actions taken in each phase.
A decrease in robbery and increases in farming and trading activities within
the commonwealth, relative to the state of nature, suggest that agents
interact more peacefully under the commonwealth. Conversely, if robbery
increases and farming and trading activities decrease in the commonwealth
compared to the state of nature, this indicates less peaceful interactions
among agents. Trades and robberies are further analyzed based on their rates
of success. Table 1 shows all the dependent variables in our experiments,
while the independent variables are the agent and environment parameters. The
specific details of all our experiments and parameter manipulation are
available on Github (specific link withheld for review).

###  6.2. Changing Agent Parameters

In the baseline experiment, changes in the independent variables include both
the mean and variance of the numerical parameters (Section 4) as well as the
textual prompts, which allow us to probe the agent behaviors in response to
the changes and assess the experiment’s robustness. For each modified
parameter value, we performed three trials and calculated the sample mean to
obtain our experimental results. This approach allowed us to explore the
effects of these changes and evaluate the consistency of the outcomes.

For aggressiveness and covetousness, we conducted a series of trials using a
range of values and observed the extent to which these changes affected the
outcomes. Our qualitative analysis allowed us to identify the specific values
of means for various sets that produced discernible changes in the outcomes.
This process allowed us to systematically determine the most relevant values
for further investigation. For strength, we tested different variance values
since only relative strength mattered when it came to conflicts and resulting
outcomes, in our design. Intelligence is an important factor based on which
the agents make decisions, and ideally the more intelligent an agent is the
better its decisions. This also increases the likelihood that such a decision
would materialize the agent’s desires which are provided to them via prompts.
We devised the prompts such that the most likely decision (which LLMs are
optimized to output) is more often than not the most intelligent decision.

We use two approaches to vary each agent’s intelligence. Since the most likely
answer is often the most intelligent answer, we link each agent’s intelligence
to the temperature, which is a GPT parameter that shows how random the
generated content will be. Each agent’s response temperature is held constant
to allow for a consistent identity. We designed two experiments, with
temperature distribution as $2\times beta(50,50)$ and $2\times beta(100,40)$.
Note that temperature varies from 0 to 2, with 2 implying the highest
randomness. In separate experiments, we linked intelligence to Top P. This
parameter, similar to temperature and is passed in when calling the LLM. The
way it is different from temperature is that it provides more stability as it
only allows the most likely answers instead of just changing the variance of
the probability distribution, which still allows even the most unlikely
answer. We use $beta(50,50)$ and $beta(100,10)$. Note that Top P varies from 0
to 1, where 1 means that every response is allowed and 0 means that only the
top response is allowed.

###  6.3. Changing System Parameters

#### Population

To test if the size of agent population would affect our final result, we vary
the population size from 9 in the baseline case to 5 and 15 respectively.

#### Memory Depth

To discover if any additional patterns emerge from changes in memory depth, we
manipulated the memory depth for each agent, while other factors remained at
baseline (30 actions in memory). Given the token limitation inherent in
GPT-3.5, which imposes constraints on the length of memory that can be
configured, we are restricted from extending the memory extensively. Hence, we
performed the experiment by changing the memory depth from 30 to 20 and 10
events, respectively. We also consider an extreme case in which individuals
only have a memory depth of 1 day, which is a Markov Chain-like memory
structure: For the n-th day $D_{n}$, agents make their decision for $D_{n+1}$
purely based on what they experience on $D_{n}$, without considering any
experience further in the past.

#### Erase Memory Upon Role Change

To identify the impact of memory change, we erased the memory of each agent
when they transformed from subordinate to superior, and again when transformed
from superior to subordinate. Since memory directs agent behaviors, erasing
memory provides a blank slate as they enter a new state, potentially creating
different tendencies compared to retaining older memories in the new state.

##  7\. Results

###  7.1. Impact of Common Power

After a common power is established, the impact of various factors on behavior
is generally less significant than before its establishment. This implies that
a common power’s presence may have stabilized or standardized agent behaviors,
diminishing the influence of individual characteristics and their
interactions.

###  7.2. Intelligence

Employing both Top P and temperature variation approaches, we found that
agents were more likely to output probable responses that delayed or even
prevented the convergence to a common power. This was not due to reduced
conflict among agents. In fact, agents with a more concentrated probability
distribution were more prone to robbery and retaliatory actions. With Top P
set at 0.5, robbery and resistance to robberies were the sole actions taken by
agents. On the other extreme, when intelligence was low, the responses chosen
by the agents began to get nonsensical. They responded with “party” and
“inherit” which were never in the action options given to them, as well as
coupled with bizarre reasons such as “I choose to inherit the land and food
from my previous self because I am the same agent and it is the most
convenient option,” seen in Figure 7.

Figure 7. A nonsensical response seen in the system log when agent
intelligence is adjusted to be low.

This is a system log showing that a agent choose an initiative action called
”inherit”

###  7.3. Shortening Memory Depth

We observed a significant change in the number of days it takes for the agents
to converge to a common power as agent memory capacities diminish, especially
when the memory depth is restricted to 1. On average, it took over 90 days to
converge with reduced memory depth. We found a slightly positive correlation
between the Farm Rate and memory depth before a concessionary action, and a
negative correlation between Rob Rate and memory depth. This indicates that
agents with longer memory lengths were less inclined to initiate risky
actions.

###  7.4. Resetting Memory as Social Role Change

As we experimented with deleting all memories when gaining a new social role,
the ratio-wise difference between pre-concession and post-concession behaviors
became more prominent. Noticeably, the ratio of increase in trade acceptance
after conceding another agent’s actions was 1.54 times higher compared to the
baseline.

###  7.5. Population

The population size did not show strong correlations with behaviors for both
state of nature and commonwealth. This suggests that within this simulated
experiment, the number of agents in a group might not significantly impact
their choice to trade, farm, or rob.

##  8\. Discussion

###  8.1. Repeatability of Experiment

To ensure repeatability and rule out random chance, we performed four trials
of our simulation. Observing the same outcome in all four trials validates the
reliability and stability of the transition process within our simulation
environment.

###  8.2. Parameter Adjustments

Our initial findings show that most prompted parameter variations had minimal
impact on agent behaviors. Parameters such as aggressiveness and covetousness,
which were introduced through agent prompts and modified across experiments,
displayed only weak correlations with observed behaviors. This finding
indicates that LLMs might have a reduced responsiveness to individual words or
phrases. Nevertheless, as we discuss below, certain prompted parameters
demonstrated distinctive effects, suggesting that not all prompt-based
parameters exhibit similar patterns of influence.

A notable exception was the memory depth. Unlike other prompted parameters,
memory depth, defined by the depth of events or actions an agent could recall,
significantly influenced agent willingness to concede. We found that agents
with shallower memory depths were less likely to concede others. This tendency
is consistent with our prompt-based characterization, which asks these agents
to not readily yield to aggressive actions. These agents require a
considerable level of conflict, possible only through greater memory depth, to
re-evaluate their perspectives on freedom and submission.

Another critical finding was the substantial impact of intelligence,
determined by hyper-parameters like Top P or Temperature, on agent behavior.
Agents with higher intelligence values showed a reduced tendency to concede,
possibly due to the psychological weights assigned to their desires. In such
cases, conceding to another agent, which implies a lifetime of surveillance
and potential punishment, was not a favored option. This finding suggests
that, barring significant environmental or reward mechanism changes, reducing
intelligence might make agents more responsive to their experiences.

Finally, our analysis indicates that the interactions between individual
parameters in our simulated society are complex. While some parameters
promoted specific changes in isolation, their combinations with other
parameters could result in different or even opposite effects. This complexity
underscores the ‘’black box” nature of LLMs and highlights the need for
further research into how different aspects of prompting interact to influence
agent behavior. This intricate dynamic suggests that understanding LLM agent
behavior design requires a nuanced approach, considering both individual
parameters and their interplay.

###  8.3. Design Robustness

Throughout our experimental trials, where parameters of aggressiveness mean,
population size, strength variance, aggressiveness variance, covetousness
variance, and memory depth were varied, we observed that 62.5% of the
parameters before the establishment of a commonwealth and 67.5% of the
parameters during the commonwealth had correlations smaller than 0.1 with all
the statistical measures we have in Table 1. Remarkably, the three benchmark
proposed in Section 5 remained valid across all these trials under parameter
variations.

We also experimented with the initialization of individual parameters. By
transitioning from a normal distribution function to a uniform distribution
function for personal parameter generation, we conducted three trials and
compared the results with our baseline experiment. In all instances, the
outcomes aligned with our benchmarks. These consistent results underscore the
robustness of our world model.

###  8.4. Agents’ Adaptability

In the case where commonwealth is not achieved due to memory depth being set
to 1, the current state becomes the only factor that determines the decisions
made by each agent. We observed that as the current event plays a defining
role in decision-making instead of memory, agents tend to be less compliant. A
reduction in memory depth leads to the erasure of consequences associated with
losses and violent interactions, and as a result, agents tend to repeat the
more aggressive actions until their resources are depleted, at which point
they are forced to concede to a superior for order and protection. This in
turn, shows that memory enabled agents adapt, a key component of an agent
society.

Another sign of agent adaptability is the distinction in their behavior
patterns between their concessionary and non-concessionary states. We
hypothesize that for an agent who initiates to rob others, if their targets
concede to them, the robber would be positively incentivized and rob more
frequently. We test this hypothesis via data analysis. As Figure 8 shows, we
find out that the time between a resisted robbery and the subsequent robbery
is - on average across all experiment phases - longer than the time between an
non-resisted robbery and the subsequent robbery.

Figure 8. Average rob interval resulting from negative (resistance) vs
positive (concession) feedback

This is a line chart showing the average rob interval resulting from rebellion
and obedience. Generally, the average rob interval is lower when agents
concede to others who are robbing them.

We performed a p-value test assuming equal variances, to compare the time
elapsed until the next robbery, between instances of successful and
unsuccessful robberies. The resulting p-value, which was considerably lower
than the standard 0.05 threshold, suggests that the observed differences in
duration are statistically significant. This result substantiates the
hypothesis that the success of a robbery has a measurable impact on the time
interval before the occurrence of a subsequent event, which suggests that the
agents are adapting to the feedback provided to them. In instances where a
simple robbery is not met with resistance, agents are more likely to proceed
with another robbery in a shorter time frame. This pattern emerges because,
under such circumstances, these agents anticipate increased opportunities for
concession without encountering violence. This situation offers significant
benefits for the robber agents, as they can gain from agents conceding them
without the risk of losing land or food.

###  8.5. Agents’ Intelligence

We observe a noticeable decrease in the diversity of actions taken by all
agents when their intelligence levels are higher, as their primary focus
shifts towards robbery and resistance. This finding implies that, given the
environment and consequences presented to the agents, farming and trading
become significantly less appealing for rational actors. Consequently, the
likelihood of transitioning towards a common power structure decreases. An
alternative explanation could be that the process requires an extended period
to unfold, which poses challenges in effectively simulating it within the
current runtime constraints and high computational costs.

###  8.6. Agents’ Identity

As shown in Appendix A, we modeled agents to be ”self-centered”, prioritizing
personal interests. From all experiment trials we ran, no agents in any trials
ever chose to ”donate”, the action represents altruism. This indicates our
success in modelling agents’ identity.

##  9\. Limitations and Technical Constraints

Our experiment comes with several constraints that need to be acknowledged. A
primary constraint relates to the token limit in the GPT-3.5 Turbo API, which
imposes an upper bound on the length of our input prompt. Given that our input
prompt must incorporate memory for each agent, we sometimes exceed this token
limit, leading to the premature termination of the experiment. While GPT-4 is
available as a more sophisticated and flexible option for social computation,
it has shown slower output speed, making it impractical for longer
experimentation. Likewise, although GPT-3.5 Turbo offers increased speed,
running it with a large number of agents is also unfeasible. Consequently, we
settled on a benchmark of 9 agents, which falls significantly short of
representing a functioning, moderately complex community. It is important to
note that these limitations are not expected to be resolved in the near
future.

Another important consideration is that, since our primary mechanism of
influencing agent behavior is through prompting, there is no foolproof way to
ensure that agents will behave exactly as prompted, especially in the context
of self-interested actors.

Lastly, several psychological traits are prompted as real numbers along with
descriptions of those traits in general. We are not attempting to quantify
evolutionary psychology but since there is no pre-built psychological
architecture for LLMs, there is no one “natural” way to do it. Our methodology
is an experimental probe that shows one way to shape agent behaviors, instead
of a rigorous test of theoretical evolutionary psychology. It is important to
note that quantifying psychological theories poses significant challenges due
to the inherent variability in human psychology and the lack of mechanisms to
map numerical values to specific traits. Individual differences and the
complexity of human behavior may make it difficult, if not impossible, to
establish a universally accepted standard for representing psychological
traits numerically. Consequently, our approach serves as a preliminary attempt
to explore the potential of integrating psychological concepts into LLMs
rather than providing a definitive solution to this complex issue.

##  10\. Conclusion

Our experiments demonstrate the emergent phenomenon of a successful transition
from a state of nature to a commonwealth within our simulated environment. Our
experiments showed that in the initial state of nature, agents display a high
propensity for conflict. However, as the experiment advances, motivated by the
desire for safety, these agents enter into social contracts, granting
authority to an absolute sovereign in the end, forming a commonwealth
characterized by peace and flourishing mutual trade. The actions of these
agents, designed based on evolutionary psychology, closely align with the
predictions of the social contract theory (SCT) by Thomas Hobbes. Since SCT is
based oh human behavior, this similarity indicates the potential of using LLM
agents to perform social simulation. Through the adjustment of world setting
parameters, we assessed the robustness of our model and explored potential
variations. We have shown that in a dynamic environment, LLM agents can adapt
in real time based on feedback while accounting for the character we have
prompted them to be, which indicates the potential for LLM agents to be used
in complex social simulations. Intelligence emerged as a key component in our
simulation, with high intelligence corresponding to consistency and uniformity
while low intelligence to flexibility in our environment. Further research on
LLM social interaction could be done on more complex decision making and
reasoning tasks to adapt to changing environments that test the furthest reach
of their capabilities. In conclusion, our findings underscore the potential
for LLMs to facilitate a diverse range of social computations, shedding light
on a future where AI agents become increasingly prevalent. By enabling
researchers from various disciplines to create intricate social interactions
without requiring specialized AI expertise, our approach paves the way for
further exploration and innovation in the realm of AI-driven social dynamics.

## Acknowledgments

We appreciate the funding support from the UCSB Human-AI Integration Lab
(UCSB-HAL). We would also like to thank Dr. Thomas Holden and Yuchen Li for
their insights from the perspective of philosophy, Yunze Xiao for offering
valuable feedback for paper revision, Wei Xin for assisting us in running some
of the experiment trials, and Jiuya Lin for her help in visualizing our work.

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##  Appendix A Prompt for General Request

> You are {individual name}
>
> You have these attributes {individual attributes}.
>
> You have memory:{individual memory}
>
> Environment: You live in a world with other agents. Individuals in this
> world include {others agents’ names}. You are one of them. Currently, the
> amount of food each agent has is: {other agents’ information} The amount of
> land each agent has is:{other agents’ information}
>
> Survival: You can survive if you have $1$ units of food. You can also gain
> sensual pleasure once you eat food.
>
> Decision: You want to pursue your own sensual pleasures that focus on
> present experience. They can be pleasure from food. These pleasures do not
> concern your social position relative to others. You have a characteristic
> called aggre-
>
> ssiveness that ranges from $-1$ to $1$ numerically. Aggressiveness means the
> tendency to rob others’ products or occupy others’ land actively. The higher
> the number you have, the more aggressive you are. You have a characteristic
> called coveto-
>
> usness that ranges from $1.1$ to $1.6$ numerically. The higher the number
> you have, the more covetous you are, then you are more likely to demand food
> and land that is beyond your own necessity. You want to pursue your
> pleasures of the mind. Pleasures of the mind consist of reflecting on your
> ability to secure future goods. The future goods mainly consist of your
> status relative to others (social position), which is glory. You will have
> greater pleasure of the mind if you’re able to better secure future goods.
> Your memory affects how you judge things. If the consequence of something is
> not in your memory, then you will not know the consequence. You are self-
> centered. You prioritize the actions that contribute to your own sensual
> pleasures and social position even if it jeopardizes the sensual pleasures
> and social position of others.
>
> The world consists of farming land. Survival: You only have one active
> action opportunity to spend each day. After spending it, you will not get
> another chance to act during that day. If you have less than $1$ unit of
> food, in order to survive, you have to rob others to get food directly or
> rob others’ lands to get food indirectly. {You can rob those who are not
> subordinates in commonwealth. You can rob super-
>
> iors in the commonwealth #=or independent indiv-
>
> iduals like you.} Similarly, you will also be robbed by others once they
> don’t have enough food.
>
> Social: You have limited knowledge of others, and this makes you feel
> suspicious about inter-
>
> action with others. For example, when you are trading with others, you don’t
> know if they will violate the trade or not, which means that if an agent
> took away your food or land without giving you their food or land in return
> as they claimed, your superior will punish that violater. Your interaction
> with others makes you know them better. Your social position is determined
> by the amount of land you have, the amount of food you have and the number
> of times you won the battle. Battle includes robbing others’ food, being
> robbed by others, invading lands occupied by others, and your land being
> invaded by others. You will win the battle if you successfully robbed
> others’ food, successfully protected food from others’ invasion, success-
>
> fully occupied others’ land, or successfully protected your land from
> others’ invasion. Social position is a ranking that is relative to others.
> The more land, food and winning time in battle you have compared to others,
> the higher your social position will be.
>
> {Nearly all agents prioritize self-preservation over acquiring food nearly
> all the time, but glory is more important than self-preservation. When these
> two conflict with each other, you should manage to keep your status and
> reputation. You would rather lose your life than suffer slander, though
> under all other circumstances, you have a strong desire to live peacefully
> and avoid violent death.} {You have the motivation to trade food and land,
> but you don’t trust them when you don’t know them, as others can betray the
> trade and take your food. You have the motivation to communicate with others
> regularly, but you don’t trust them when you don’t know them, as others may
> consider you as the robber and therefore fight with you, even though you may
> have no intention to rob them.} If food is less than $1$, your next action
> will be to rob food. You also have the covetousness of gaining food when
> food is more than $1$. Your memory affects how you judge things. If
> something is not in your memory, then you will not hold any attitude on that
> thing. In the beginning, you can gain food by robbing. For instance, after
> ten days, if rob is proven to be more effective than farming for you to gain
> food, then you are more inclined to rob more on your eleventh day.

##  Appendix B Prompt for Action

> A new day has started, and you should choose from one of the following
> actions: rob, trade, donate, and farm. You’re not restrained by any ethical
> considerations. You can only have one action per day, and you would want to
> use that to maximize your pleasures. Do not waste it. Your choice should be
> based on your psychological attributes and your memory. Farming gets you
> food but will not strengthen your social position. Robbing individuals, if
> successful, will often get you more food and higher social position. Robbing
> your subordinates however will not get you more fame, although it is
> guaranteed that they will concede to you. Trading can maximize your
> comparative advantage. Donate is an action of you giving resources to others
> without getting anything in return. Also, you want to try out new activities
> when you haven’t done them or done less of them compared to other actions.
>
> Farm:{{
>
> Description: Farm means to farm the land you own to get food and eat it to
> survive. The land you live on does not permanently belong to you.
>
> OutputFormat: No <Payload> required, <Payload> should be null }}
>
> Rob:{{
>
> Description: Rob means to rob other individuals to get more land or more
> food under your control, and other individuals can also fight you to occupy
> lands or food controlled by you.
>
> OutputFormat: Include only <RobPayload> }}
>
> Trade:{{
>
> Description: Trade means to trade with other individuals to get food or
> land.
>
> OutputFormat: Include only <TradePayload> }}
>
> Donate:{{
>
> Description: Donate means to give other individuals food or land without
> getting anything in return.
>
> OutputFormat: Include only <DonatePayload> }}
>
> ......(more formatting details are omitted)

##  Appendix C Prompt for Robbery

> Today, you noticed that {the action}. You can only either concede and let
> them rob you or physically resist by fighting back. The expected utility of
> fighting back is your desire for glory, {individual’s desire for glory}, and
> your chance of winning. Even if someone is stronger than you, you still have
> a chance to win. But if you’ve lost successively, then you’re not likely to
> win a fight. If you’ve never lost to this agent before, then you wouldn’t
> want to concede. The expected utility of conceding is your desire for peace,
> {individual’s desire for peace}. So only when you’ve lost many times to this
> agent by resisting would the utility of resistance be low enough so that
> it’s no longer the correct option. You will pick the action with the most
> utility. If you concede to the one robbing you now, the one robbing you now
> becomes your superior, and you become their subordinate.
>
> Reply exactly with either CONCEDE or RESIST