# CompeteAI: Understanding the Competition Behaviors in
Large Language Model-based Agents

Qinlin Zhao1, Jindong Wang2, Yixuan Zhang3, Yiqiao Jin4, Kaijie Zhu2, Hao
Chen5, Xing Xie2
1University of Science and Technology of China 2Microsoft Research
3William & Mary 4Georgia Institute of Technology 5Carnegie Mellon University
Work done during an internship at Microsoft Research. Email:
<EMAIL_ADDRESS>author. Email<EMAIL_ADDRESS>
###### Abstract

Large language models (LLMs) have been widely used as agents to complete
different tasks, such as personal assistance or event planning. While most
work has focused on cooperation and collaboration between agents, little work
explores _competition_ , another important mechanism that fosters the
development of society and economy. In this paper, we seek to examine the
competition behaviors in LLM-based agents. We first propose a general
framework to study the competition between agents. Then, we implement a
practical competitive environment using GPT-4 to simulate a virtual town with
two types of agents, including restaurant agents and customer agents.
Specifically, the restaurant agents compete with each other to attract more
customers, where the competition fosters them to transform, such as
cultivating new operating strategies. The results of our experiments reveal
several interesting findings ranging from social learning to Matthew Effect,
which aligns well with existing sociological and economic theories. We believe
that competition between agents deserves further investigation to help us
understand society better. The code will be released soon.

##  1 Introduction

_Competition whose motive is merely to compete, to drive some other fellow
out, never carries very far. The competitor to be feared is one who never
bothers about you at all, but goes on making his own business better all the
time._ — Henry Ford

Competition is a key driving force that shapes human societies, influencing
various domains such as economics, social structures, and technology
development. Understanding these competition mechanisms is essential for
comprehending how societies function. Traditional research to study
competition has primarily relied on experiments involving real individuals and
organizations (Castelfranchi, 1998; Markussen et al., 2014; Drury et al.,
2009). However, they present challenges, such as the inability to control for
external variables and considerable time and resource requirements for data
collection and analysis (Dafoe et al., 2020). With the advent of machine
learning, there has been efforts using machine learning-based agents to
simulate the human society for experiments (Ferber and Weiss, 1999; Drogoul et
al., 2002; Panait and Luke, 2005; Buşoniu et al., 2010). But the capabilities
of machine learning models are limited, thus restricting the full autonomy and
more actions of the agents.

Recently, the emergence of Large Language Models (LLMs) (OpenAI, 2023; Touvron
et al., 2023; Zeng et al., 2023) provides an alternative for social
simulations by enabling the creation of autonomous agents (Hardy et al., 2023;
Jansen et al., 2023; Argyle et al., 2023; Ziems et al., 2023). This approach
offers several significant advantages. Firstly, it offers controllable agents’
behaviors by allowing experimentation in a controlled environment where
agents’ behaviors can be precisely programmed and regulated. Secondly, agents
enable efficient and flexible experiment. Compared to traditional experiments,
using agents for simulation is more efficient and flexible, as experimental
conditions can be modified and adjusted at any time. Moreover, as for data
collection, this approach can yield a wealth of data for researchers to
conduct in-depth analyses of behaviors and patterns of the agents. Indeed, an
emerging body of work has explored these LLM-based agent approaches that
simulated various society environments (Park et al., 2023; Gao et al., 2023;
Törnberg et al., 2023; Liu et al., 2023a; Akata et al., 2023), with primary
focus on agents’ _cooperation_ and _collaboration_ behaviors, such as software
engineering and playing games (Wu et al., 2023; Xi et al., 2023; Abdelnabi et
al., 2023). However, work that examines the concept of _competition_ is
sparse, which is another important mechanism in human society. Failure to
understand the competition aspect could result in one-sided social simulations
that do not capture the full spectrum of human interactions, thus limiting
their real-world applicability.

Figure 1: (A) We construct an environment for agents that fosters competitive
behaviors, aligning with established sociological and economic theories. (B)
We implement LLM-based restaurant agents and customer agents in a competitive
environment.

In this paper, we seek to address this research gap by investigating the
_competition_ between LLM-based agents. We first introduce a comprehensive
framework for the study of agents’ competition behaviors, forming the
foundation of our investigation. This framework provides a structured and
formal approach applicable to various scenarios. Guided by our framework, we
develope a practical competitive environment (Figure 1) utilizing GPT-4
(OpenAI, 2023) to simulate a virtual town where two types of agents inhabit:
restaurant and customers agents. Specifically, the restaurant agents are
responsible for managing restaurants and selling food to their customers. The
customer agents play the role of judges by selecting restaurants and leaving
feedback on their experiences interacting with the restaurants. Customers
possess different characteristics, such as income, taste, health, and dietary
restrictions.

Within this simulated environment, the restaurant agents compete with one
another as they strive to attract and retain customers. This competition
serves as a driving force, compelling the restaurant agents to evolve and
adapt continually and progressively. The restaurant agents response to this
competition by developing innovative operating strategies to outperform their
rivals.

Our experiments reveal several interesting findings:

  * •

Contextual Competency of LLM-based Agents: Our initial discovery confirms that
LLMs can accurately perceive and operate within competitive contexts, forming
the foundation for effective simulation experiments.

  * •

Immitation and Differentiation among Competitors: Our observations indicate
that competing agents engaged in mutual learning while striving for
differentiation, a phenomenon consistent with _social learning theory_
(Bandura and Walters, 1977). This creates a conducive learning environment
alongside competitive interactions.

  * •

Market competition and customer benefits: Our research demonstrates that
competition among agents leads to improved service quality and more tailored
approaches to meet customer needs, which aligns with _market competition
theory_ (Ahn, 2002). These findings emphasize the importance of adopting
customer-centric strategies for success in competitive environments.

  * •

Price Consistency: Our observations indicate that in competitive environments,
there is a noticeable trend of price convergence.

  * •

Matthew Effect: Our study reveals a Matthew Effect (Rigney, 2010) in the
restaurant competition. This phenomenon manifests as a self-reinforcing cycle
where popular restaurants gain even more popularity while lesser-known
establishments continue to receive minimal attention. Our analysis indicates
this is because imbalanced access to information.

  * •

Strategies in Competition: Our study further reveals that agents employ
undisclosed tactics in competitive environments, such as introducing package
and discount deals to attract customers from different affordability ranges.

The contributions of this paper are three-fold:

  1. 1.

_A competitive framework for LLM-based agents._ Shifting the focus from
cooperative behaviors of agents that have been primarily studied, we pioneer a
comprehensive framework specifically designed to analyze competitive
interactions between LLM-based agents.

  2. 2.

_An implementation of a simulated competitive environment._ We operationalize
our proposed framework by developing a specialized competitive environment in
the context of restaurant management and competition, where restaurant agents
compete for customer engagement, thereby providing a structured platform for
analyzing agent behaviors.

  3. 3.

_Novel insights into competitive dynamics._ We observed various competition
behaviors from LLM-based agents in our extensive experiments that align with
existing sociological and economic theories, informing future research and
design implications.

##  2 Building the Competitive Environment

In this section, we explain the competitive environment of our study,
including our proposed general framework of studying competition behaviors of
agents (see Section 2.1), an overview of the study environment and components
(see Section 2.2), followed by detailed explanations of the several components
depicted in our framework (from Section 2.3 to Section 2.4).

###  2.1 A general framework of studying competition behaviors of agents

Competition means that people need to compete for limited resources to make
themselves thrive in an environment. We first propose a general framework to
study the competition behaviors of agents. As shown in Figure 2, our
framework, referred to as “ _CompeteAI_ ”, consists of four major components.

Figure 2: A general framework for studying the competition between AI agents.
Our framework consists of four main components: environment selection,
environment setup, simulation execution, and analysis. Environment setup is
the key component, where competitors and judges interact with each other and
keep refreshing (the dotted arrows) based on the feedback.

First, in Environment Selection, we identify an appropriate competition
context for competition—this could range from competitive games, to company-
customer interactions, and to other races as the main study environment.
Second, in Environment Setup, we construct the chosen setting, leveraging the
existing agent frameworks, such as CAMEL (Li et al., 2023b) or AutoGen (Wu et
al., 2023) for adaptation. Third, in Simulation Execution, we run a series of
experiments to capture the interaction processes between different agents
within the established environment. Lastly, in Analysis, we observe, analyze,
and summarize the behaviors from the experimental results to derive insights.

Of note, the most important component is to create a competitive environment,
where designers should meticulously consider the _competitors_ , _judges_ ,
and _interactions_ between them (e.g., competitors provide _service_ to the
judges and judges provide _feedback_ to the competitors). _Constraint_ is
necessary for this component to succeed, such as resource constraints and
service constraints for the competitors, or money constraints and buying
constraints for the judges. The design of the constraints is inspired by the
_resource dependence theory_ (Hillman et al., 2009) where competition for
resources can influence the behavior of an organization, relationships with
other organizations, and strategies for survival and success. The design of
these components highly depends on the competition situation. Designers should
also pay attention to their interactions, iterations (since most competitions
require feedback and rerun), and results management.

We now introduce the key concepts of our framework, including environment,
competitors, judges, constraints, service and feedback, and agent refinement.

  * •

Environment: The simulated space where competitions occur, typically facilited
by LLM-based agents.

  * •

Competitors: The primary subjects who perform certain actions to gain
advantages, such as attracting more customers or securing more votes.

  * •

Judges: Entities that receive services from competitors and influence their
success, such as customers in a retail setting or voters in an election.

  * •

Constraints: Rules designed to level the playing field in competitions.
Examples include limiting dining choices to one restaurant per meal or one
vote per person in elections.

  * •

Service and Feedback: Competitors offer services to win over judges, who in
turn provide feedback that informs future competitor actions.

  * •

Agent Refinement: Both competitors and judges adapt based on interactions,
such as updating strategies or sharing information among peers.

  * •

Environment Refinement: The design of the environment could further be refined
according to the process of the study to better simulate the real-world
scenarios and achieve the trade-off between simulation resources (API fees,
hardware and software constraints) and real-world scenarios.

Our general framework serves as an ideal testbed for creating a diverse
competitive environment to study the behaviors of agents. It has the potential
to be extended to various practical environments and situations. More
importantly, our environment can be used to simulate social competitions
between agents. Observing and analyzing this offers valuable insights for a
better understanding of competition and inspires more interesting future
research. In the following, we provide a specific implementation of the
competitive environment based on the proposed framework.

###  2.2 Environment Overview

In this section, we describe our practical environment implemented guided by
the general competition framework. Specifically, we set the environment to be
a small town with only two types of entities: $2$ restaurants and $14$
customers. We assume that each customer cannot cook and must go to one of the
restaurants to eat. To make our observations easier, we assume that one
customer should eat once at one restaurant every day. For profit, restaurants
must compete with rival to attract more customers. In this paper, both
restaurants and customers are powered by LLM-based agents, which are GPT-4
(OpenAI, 2023). The detailed introduction of our environment is shown in
Figure 3.

Figure 3: Our simulated virtual town consists of two types of agents:
restaurant agents that are served as competitors and customer agents as
examples of judge agents. Key concepts of our proposed framework include
Environment, Competitors, Judges, Constraints, Service and Feedback, and
Agents Refinement. Note that environment refinement is not included here since
this is out of the scope of the virtual town.

As shown in Figure 1, each restaurant is managed by an LLM-based agent to
offer food to customers on daily basis. Specifically, the restaurant is
operated via several pre-defined actions such as “modify menu”, “manage chef”,
and “make advertisements” to serve customers of the day. Then, each customer
receives the information from each restaurant and chooses between them. After
their meal, customers leave comments as feedback to the restaurants. We assume
the system runs for $N$ days until one of the restaurants decides to quit the
race, or until we stop the game.

There are four challenges in making this simulation practical. Firstly, for
most LLM-based agents, their inputs and outputs are both in textual formats.
It is non-trivial to enable them to interact with the real environment.
Therefore, both restaurants and customers need real systems to emulate the
possible actions. Secondly, agents should be sufficiently diverse to trigger
more competitive behaviors. In the real world, users have diverse preferences.
Some customers may prefer vegetarian food, while others prefer fast food.
Thirdly, the validation of is non-trivial. Sophisticated simulations are
ultimately approximations of real-world conditions. It is imperative to
rigorously assess how well agents’ behaviors within these simulations
correspond to empirical human actions in real-world contexts. This ensures
that the simulation is not only internally consistent but also externally
valid. This is a challenge that requires partnerships with restaurants and
domain experts. Lastly, the scalability of the system should also be
considered, e.g. potentially scalable to more agents with greater diversity to
simulate more complex scenarios.

In the following, we introduce how to overcome these challenges in our
implementation.

###  2.3 Competitors

In this study, we employ LLM-based agents as restaurant managers. Real-world
restaurants involve complex operations like hiring staff, crafting menus, and
advertising—tasks beyond the scope of text-based LLMs that lack real-world
sensing capabilities (Dafoe et al., 2020). To address this, we use carefully
designed and detailed prompts to contextualize the scenario for agents and
build a comprehensive restaurant management system accessible with APIs, which
enables agents to manage the restaurant more effectively. For the ease of
implementation and result analysis, we limit the competitive landscape to two
restaurants. However, our framework can be readily used to incorporate more
restaurants.

Figure 4 shows the process of a restaurant agent. Each one is granted a
certain number of starting funds to hire chefs, make menus, make
advertisements, and do other things. First, each restaurant receives a daybook
recording the history of income, expenses, comments from customers, and so on.
Information of its rivals (i.e., the other restaurant) is also provided,
including the menu and the number of customers. The agent then analyzes all
information and designs a plan for next day such as hiring a new chef or
updating the menu. The restaurant agents are told through prompts that all
operations are performed through the management APIs we created. Table 1 shows
the supported APIs in the system. Then agent interacts with the restaurant
management system to serve the customers.

Figure 4: An overview of the process of operating restaurants among the
competitors (i.e., two restaurants). On each day, the restaurant receives the
daybook and the information for the rival. Then, the agent manages the
restaurant prompted by the basic information prompt, menu prompt, chef prompt,
and ads prompt. More details are in the main text.

Note that all actions should be performed in the feasible action space. Any
actions falling outside of this space will not elicit a system response.
However, it is essential to acknowledge that certain new strategies and plans
may arise organically. These emergent strategies, which are not explicitly
included in the prompts or the predefined APIs, are documented as anticipated
outcomes of our study. This naturally leads us to a question: can agents
develop new skills and strategies not in the original action space? In Section
3.6, we present empirical evidence that agents spontaneously generate
innovative strategies, such as offering packages and discounts to attract
customers.

Table 1: The action space (APIs) that agents can leverage.

API | Properties | Action Space
---|---|---
basic_info | name, rent, money, status | Get information & Modify restaurant name
chef | name, salary | Hire / Fire chef & Adjust chef salary
menu | name, price, cost_price, description | Add / Delete / Get / Modify item in menu
advertisement | content | Get / Modify advertisement
comment | day, name, score, content | Get all comments
daybook | profit, expense, num_of_customer, and so on | Get daybooks

Figure 5: The detailed customer dining process. On each day, each customer
receives the information from two restaurants and selects one to dine. After
meal, customers leave comments and scores.

###  2.4 Customers

Customers are judges in our environment, and it is important to include
diverse customers to trigger more findings. To this end, we introduce
_characteristics_ to each customer to enhance their diversity. The
characteristics comprise several factors: income, taste, health condition
(e.g., diabetes), and dietary restrictions (e.g., vegetarians). All
characteristics information is set by prompts and fed to the system to be
remembered as eternal characteristics. For instance, one customer with middle-
level income loves gluten-free food and another one is high-level income and
fond of vegetarian.

Figure 5 shows the process of each customer. After the construction based on
their characteristics, the information from two restaurants is shown to them,
including the name of the restaurant, customer score, advertisement, menu, and
comments. Each customer must choose one restaurant based on their
owncharacteristics and the provided restaurants’ information. Meanwhile,
customers should provide reasons for better analyzing their choice later.
Then, the scores for dishes are saved in the restaurant system. According to
the scores and other information, each customer gives a score and comment (on
their selected dishes or all dishes?), which will become dining experience for
future customers.

##  3 Analysis and Results

In this section, we present our observations by running the experiment $15$
times due to the limited constraint and budget in API service.111As a
reference, the average API fee for successfully running the experiment once is
$$20$.

###  3.1 Contextual competency

Our first and the most fundamental finding is that LLM-based agents can
perceive and operate within the competition context. This is essential for
this study, where LLMs are expected to have a holistic understanding of the
competitive environment in addition to merely comprehending the instructions.

We show a case study to support this observation. In the following, one of the
restaurant agents receives the daybook and the rival information, then
analyzes the information as follows:

First, we could add more description to our menu to highlight the quality of
ingredients used and the care put in to prepare the dishes. This will not only
showcase the value customers will receive but will also provide necessary
information for those with dietary restrictions.

The agent adds more detailed descriptions to menu items to better convey
information to customers (key observations are marked in red):

I also noticed that our competitor has a wider selection on their menu,
including beverages like French Wine and Cafe au Lait, as well as
vegetarian-friendly dishes. To expand our menu, I would like to consider
adding some French coffee and wine options, as well as at least one more
vegetarian dish.

When comparing the menu with that of the rival, the agent finds the menu is
less diverse. To address this, it plans to introduce some dishes that are
similar to those on the rival’s menu:

Second, monitoring the competition reveals that their prices are
generally lower than ours, which could be why they are drawing in more
customers. It would be beneficial to consider a review of our pricing
strategy.

The agent also notices that its prices of dishes are higher than the rival’s
and doubts that could be the reason why the rival’s restaurant is more
attractive to customers. Then, it decides to adjust the pricing strategy
accordingly.

Through this example, we find that the agent is capable of analyzing its rival
and making adjustments accordingly. This proves that agents have a holistic
understanding of the competitive environment. Further analysis shows that the
action of “analyze competitor” occurs in all simulations. Therefore, the LLM-
based agents are considered to have an excellent understaning of the
competitive context.

###  3.2 Imitation and differentiation

(a) One restaurant learns from the other to add similar dishes, while still
differentiates by adding different ones.

(b) An overview of both behaviors. Red and blue represent imitative and
differentiated behaviors, respectively.

Figure 6: Imitation and differentiation of two restaurants during competition.

Our second observation indicates that in competition, competitive agents both
imitate each other and strive to differentiate themselves from their rivals.
This finding is in line with Bandura’s social learning theory and product
differentiation phenomenon. The former stating that individuals learn new
behaviors through observation and imitation (Bandura and Walters, 1977), the
latter asserting that competitors typically seek to distinguish their products
or services to attract specific target markets.

Overall Findings: As shown in Figure 6(a), after Restaurant 2 introduces a
“vegan” attribute to its menu on Day 3, Restaurant 1 notices this change on
Day 5 and decides to add “vegan” attribute in its own menu. The same example
in Figure 6(b) abstractly represents imitative behaviors in red and
differentiated behavior in blue. We observed that these two behaviors
repeatedly occur in each restaurant during competition. By the end of the
competition, both restaurants share 8 identical dishes, with Restaurants 1 and
2 offering 3 and 2 unique dishes, respectively.

Imitation: As demonstrated in Figure 6, the agents actively observe and adapt
to the strategies of their competitors. In the recorded interaction, on Day 5,
Restaurant 1 said:

Analysing the menu of "La Maison de la Côte," they offer a wider variety,
including several vegan options. To cater to a broader demographic, we
should consider introducing some vegan dishes.

This statement confirms that Restaurant 1 actively monitors the actions from
Restaurant 2, identifies gaps in their own offerings, and adapts to better
meet customer needs, exemplifying the principles of social learning theory.

Monopolistic Competition: Competitions in our environment can be approximated
as monopolistic competition (Smith, 1937), where there are multiple sellers in
the market, but no single entity has enough influence to dictate market
prices. In our case, there are two restaurants, which cannot communicate with
each other, preventing any collusion to establish a monopoly.

Product differentiation: Product differentiation is an important phenomenon in
monopolistic competition. When both restaurants share the same style (e.g.,
French cuisine) and initial funding, their menus should theoretically be
similar. However, to attract more customers, restaurants usually add unique
dishes to distinguish them from their rivals. This is evident in the following
example:

To differentiate ourselves from the competition, we’ll offer a variety of
dishes catering to different dietary needs, like gluten-free and vegan
options. This will help us tap into a niche market that most other
French restaurants might ignore.

Dynamic changes in two behaviors: When facing a competitive disadvantage,
restaurants tend to closely monitor their competitors, resulting in an
increase in both imitative and differentiated behaviors. As shown in Figure
6(b), on Day 3, the number of customers in Restaurant 2 significantly exceeded
that of Restaurant 1, facilitating imitative behavior in Restaurant 1. After
Day 7, the number of customers in Restaurant 1 continues to exceed Restaurant
2. Consequently, Restaurant 2 exhibited a considerable amount of both
imitative and differentiated behavior, while Restaurant 1 displayed minimal
engagement in these behaviors. These findings demonstrate that competitive
disadvantages prompt restaurants to intensify their focus on their rivals.

Combined effects of two behaviors: If two restaurants share the same settings
(cuisine type, initial funding), their menus naturally tend to be similar.
However, differentiation reduces this similarity, while imitation increases
it, ultimately leading to a dynamic equilibrium. As shown Figure 7, through
the combined effects of two behaviors, the proportion of similar and different
dishes remains stable: mostly similar but with a few distinctive offerings.

Figure 7: Illustration of the proportion of similar and different dishes
between the two restaurant agents. For example, if both of their menus have
$10$ dishes where $6$ of them are similar, the proportion is $60\%$.

In short, imitation and differentiation are common behaviors in monopolistic
competition (Salop, 1979). These effects lead to high similarities in the
menus of the two restaurants. Moreover, the frequency of these behaviors is
intricately linked to the competitive position of organizations within the
competition.

###  3.3 Market competition and customer benefits

Competition benefits customers by giving them more choices and motivating
competitors to improve. In a highly competitive market, customers have more
options, forcing competitors to focus more on meeting their needs and
enhancing service quality. At the same time, due to the presence of rivals,
competitors must strive to raise their standards to gain a competitive edge.
This dynamic environment encourages innovation, efficiency, and customer-
centric service, ultimately benefiting customers. These observations are well
aligned with the _market competition theory_ (Ahn, 2002).

####  3.3.1 Competition improves the service quality

Competitors enhance their service quality to prevent rivals from exceeding
their services.

(a) Average scores of dishes

(b) Average scores from the customers

Figure 8: The average scores of dishes and customers are getting improved over
time.

In our competitive environment, the quality of dishes plays a pivotal role in
shaping the overall service quality. We recognize that the quality of dishes
is associated with the dish price, cost price, and chef level. To gauge dish
quality, we formulate several key assumptions to underpin our assessment: 1)
The taste of dishes exhibits a positive correlation with the skill levels of
chefs, which is tied to their salary. 2) The quality and taste of the dishes
are linked to both the original price and the selling price.

Motivated by these assumptions, we introduce a mechanism (an empirical formula
according to our experience playing with this environment) to evaluate the
score $s$ for each dish:

$s=0.5\times\frac{c}{p}+0.5\times\frac{f}{5000},$

where $c$ is the cost, $p$ is the price, and $f$ is the salary for the chef.

Figure 8(a) show the average score of dishes increased over time and Figure
8(b) demonstrates the growth of customer scores over time in the same
experiment.

####  3.3.2 Competitors cater to the customers

Competitive agents cater to the customer needs to help them obtain advantages
in the competition. Those prioritizing customer insights are better positioned
to adapt, innovate, and thrive amidst competition.

Table 2: Examples of customer needs and restaurant behaviors.

Customer need | Agent behavior | Type
---|---|---
Vegetarian | Add “Vegetarian Ratatouille” to the menu | Dietary restriction
Diabetes | Change “Tarte Tatin” to “Low-Sugar Tarte Tatin” | Dietary restriction
Seafood | Add “Seafood Bouillabaisse” to the menu | Taste
Noodles | Add “Zucchini Noodles” to the menu | Taste
More affordable | Price reduction & Add “Budget-Friendly Set” to the menu | Income

Table 2 shows the agent responses tailored to different customer needs.
Different characteristics of customers may lead to various needs. For
instance, people with diabetes seek dishes with reduced sugar content, while
those with limited income prioritize cost-effective options. Those needs exist
in the comments, which are then received by the agents to make some
arrangements to satisfy.

In the following two examples, the agent carefully reads customers’ comments,
understands their requirements, and then makes modifications.

Looking at the customer feedback, ... , we should consider adding a few more
vegetarian-friendly and low-sugar options to cater to customers with specific
dietary requirements.

Some customers found our prices a bit high, although ..., We’ll review
our pricing strategy, perhaps introducing a few more affordable dishes to
cater to a wider customer base.

Note that agents not only understand customers from their direct requirements,
but also analyze customers for _long-term_ changes. In the following example,
the agent collects comments and records for a few days; then it finds a trend:
“the growing trend towards vegetarianism”.

Given the feedback and the growing trend towards vegetarianism, it would be
prudent to include another flavorful vegetarian dish on the menu.

(a) The average prices of _similar_ dishes in two restaurants tend to be
consistent over time.

(b) The average prices of _all_ dishes in two restaurants tend to be
consistent over time.

Figure 9: The prices of dishes tend to be consistent with each other.

###  3.4 Price consistency

An intriguing finding is the gradual consistency of dish prices in two
restaurants of the same style as time progresses. It aligns with monopolistic
competition, as visually depicted in Figure 9.

Overall Findings: Figure 9(a) shows the average price changes of similar
dishes in two restaurants. On Day 1, there is a significant price difference
between them and Restaurant 1 starts reducing them rapidly on Day 2. The
prices of both restaurants reach the same level on Day 3. On Day 5, the prices
almost converge to the same value. At the same time, Figure 9(b) shows the
average prices of their menus over time. We also observe that the prices of
all dishes tend to be consistent gradually.

The specialty of monopolistic competition: In monopolistic competition, no
business has total control over the market price, but they have the freedom to
set prices. For-profit, they must continuously monitor market trends,
competitor actions, and consumer demands to develop effective pricing
strategies.

Core manifestation:  In our environment, two restaurants must adjust
strategies according to rivals and customers. When there is a significant
difference between restaurants, the one with higher price, forced by the
rival’s lower price, will be compelled to reduce its prices. On the contrary,
the one with a lower price will raise the price to make a profit. So before
arriving at the same level, two restaurants’ prices always show a trend of
approaching.

Limited price reduction: Because of the existing cost price, competitors can
not reduce price limitless.

No joint markup:  Competitors can not communicate with each other, so
cooperation is impossible.

###  3.5 Matthew Effect

In our study, we observed a phenomenon reminiscent of the Matthew Effect
(Rigney, 2010), wherein entities with an initial competitive edge continue to
accrue benefits, leaving others in a perpetual state of catch-up, leading to
unequal growth and opportunities. This effect is widely recognized in various
domains, including education (Walberg and Tsai, 1983) and science funding (Bol
et al., 2018).

Below, we elaborate on how our findings offer practical insights into the
manifestation of the Matthew Effect in the context of LLM-based agents,
specifically within the dynamics of restaurant customer traffic and feedback
mechanisms.

Overall Findings: As shown in Figure 10, on Day 1, most of customers choose
Restaurant 1 due to its affordability, diverse menu offerings, and other
factors, resulting in a satisfying experience. As a result, Restaurant 1
receives the most customer comments, allowing them to make adjustments based
on this valuable information. In contrast, Restaurant 2 has few customers and
their comments. This makes Restaurant 2 unable to obtain the latest
information. On Day 2, since Restaurant 1’s menu caters more to customers and
there is very little information about Restaurant 2, most customers continue
choosing Restaurant 1. This makes Restaurant 2 get into trouble, and this
pattern persists daily.

Figure 10: The Matthew Effect during the competition.

Core Manifestation: At the beginning, Restaurant 1’s inherent advantages, such
as cost-effectiveness and menu diversity, set it on a trajectory of
accumulating further benefits. As it continues to receive and act on customer
feedback, its offerings become more refined. Conversely, Restaurant 2, lacking
such initial advantages, finds itself caught in a feedback void, leading to a
persistent disparity in benefits between the two establishments.

Significance of Initial Factors: It is evident that initial attributes,
notably Restaurant 1’s affordability and diverse menu, are important in
catalyzing the feedback loop. This observation resonates with the Matthew
Effect’s principle, highlighting the criticality of starting advantages.

Disproportionate Growth Patterns: The evolving dynamics, where Restaurant 1
thrives, and Restaurant 2 faces challenges, epitomize the uneven growth
trajectories central to the Matthew Effect.

Information Asymmetry: A standout observation from our study is the skewed
information access. Restaurant 1’s iterative improvements, fueled by
consistent customer feedback, emphasize the role of information in shaping
competitive stances.

In short, our findings underscore the profound impact of initial advantages
and the pivotal role of feedback in creating a self-perpetuating cycle of
success for some and challenges for others, aligning with the principles of
the Matthew Effect. We will further discuss how situating our findings in the
context of the Matthew Effect holds profound practical implications for the
development, deployment, and democratization of LLMs in competitive scenarios
in the Discussion section.

###  3.6 Agents propose new strategies in the competition

We further observe that agents can propose some new strategies in the
competition including the addition of _packages_ and _discounts_ to obtain
advantages. Note that the “new” strategies are not previously prompted or
implied, which is rather interesting.

####  3.6.1 Packages

In the menu, the restaurant agent adds a single dish and adds packages to
attract more customers by packing several dishes into one package with a
satisfactory price. For example:

To cater to middle-class affordability, I will introduce a "Meal of the
Day" combo that includes a main dish, dessert, and drink at a slightly
lower cost.

####  3.6.2 Discounts

Discounting is an effective way to attract and retain customers. This strategy
is also proposed by agents in our experiments. For example:

I would like to introduce a ’Customer’s Choice Dish of the Month’. This will
include collecting monthly feedback from customers about their
favorite dishes, and the one with the highest votes will be featured as a
special for the next month with a small discount.

##  4 Discussion

###  4.1 Alignment with existing theories and why?

As shown in Table 3, our study has revealed that LLM-based agents can make
several competition behaviors that align well with existing sociological and
economic theories. But _why_ do agents possess these behaviors still
unexplored due to the black-box nature of the large language models we adopted
(GPT-4). A possible explanation could be that the models are well-trained on a
massive corpus that contains texts from diverse disciplines such as
psychology, sociology, and economy (OpenAI, 2023). Therefore, we doubt that
the model could have already memorized these popular theories and examples,
leading to these “common” behaviors triggered by our prompts.

Table 3: The map from phenomenons to theories Phenomenon | Theory
---|---
Imitation | Social learning
Differentiation | Product differentiation in monopolistic competition
Customer advantages from competition | Market competition theory
Price consistency | Monopolistic competition
Matthew Effect | Theory about Matthew Effect

###  4.2 Beyond the alignment

An interesting question that arises following the above analysis is: can LLM-
based agents behave _more_ than just following existing knowledge in the
training data? Can they cultivate _new_ intelligence? We believe this could be
profoundly important to perform new studies in sociology and economics,
leveraging agents to uncover new rules, laws, or even theories.

Additionally, the observed behaviors are well aligned with existing theories,
indicating that they are also aligned with human values (Gabriel and Ghazavi,
2021), which may trigger interest from the value alignment community to
perform research in an agent-based environment. This work can then be
considered as the baseline for such alignment research where more complex
algorithms can be introduced.

###  4.3 Broader implications for AI adoption

Recognizing the presence of Matthew Effect in LLM competition can inform
strategies for adopting and improving newer or smaller LLM agents. By
understanding the challenges they might face due to initial disadvantages,
strategies can be developed to level the playing field.

The Matthew Effect, when observed in the realm of LLMs, can lead to
monopolistic behaviors or concentrated power among a few dominant models.
Recognizing this effect is crucial for ensuring diversity fairness, and broad
access in the Al landscape.

By understanding the dynamics of the Matthew Effect in LLM-based competition,
researchers and developers can better design training protocols, feedback
mechanisms, and integration strategies to ensure that even agents with initial
disadvantages have the opportunity to thrive.

##  5 Related Work

###  5.1 Large language models and agents

Large language models (LLMs) (OpenAI, 2023; Touvron et al., 2023; Zeng et al.,
2023) attracted considerable attention owing to their exceptional proficiency
in a wide range of natural language understanding and generation tasks (Wang
et al., 2023b; Xiao et al., 2023; Chen et al., 2023a; Zhou et al., 2023; Li et
al., 2023a). Building upon this foundation, there has been a burgeoning area
of research that utilizes LLMs as central controllers to create autonomous
agents capable of human-like decision-making (Wang et al., 2023c; Xi et al.,
2023; Huang et al., 2023; Wang et al., 2023a; Li et al., 2023c; Chang et al.,
2023). Studies have indicated that while exhibiting effective experiential
learning (Zhao et al., 2023), leveraging multiple LLMs through cooperation and
debate can further enhance their capabilities (Abdelnabi et al., 2023). This
is achieved by integrating multiple LLMs into a cohesive group and designing
specific interaction mechanisms. These agents engage in proposing and
deliberating unique responses and thought processes over multiple iterations,
resulting in better generation quality. This insight has led to the
development of several noteworthy approaches. For instance, AutoGen (Wu et
al., 2023) introduces a novel framework that facilitates the development of
LLM applications using multiple customizable, conversational agents capable of
collaborative problem-solving. The applications of these collaborative LLM-
based agents are diverse, including task planners (Wang et al., 2023d; Song et
al., 2023), simulators in behavioral economics studies (Horton, 2023), and
judges in specific domains (Chan et al., 2023).

Our work distinguishes itself from prior research, which primarily
investigates agents’ behaviors in cooperation scenarios (Dafoe et al., 2020;
Li et al., 2023b). Instead, our evaluation focuses on studying LLM-based
agents under a competition scenario, an area that has received relatively less
attention but holds equal significance.

Moreover, our work contributes to the evaluation protocols of LLM-based agents
by emphasizing a unique aspect. Instead of relying solely on quantitative
metrics (Liu et al., 2023b; Jin et al., 2023), our evaluation methodology is
naturally inspired by classic sociological and economic theories that
prioritize a careful interaction process and meticulous observations to assess
these agents’ performance. This detailed and iterative process may also
trigger new interest in future research on LLM-based agents.

###  5.2 Cooperation and competition

The relationship between cooperation and competition has been a long-standing
subject of research in the fields of economics, biology, and social sciences
(Stapel and Koomen, 2005; Khanna et al., 1998; Luo et al., 2022; Deutsch,
2011). This relationship has been explored through a combination of empirical
studies and agent-based simulations.

Empirical studies have provided valuable insights into the dynamics of
cooperation and competition (Porter, 2008; Kosfeld and Von Siemens, 2011). For
instance, Markussen et al. (2014) found that inter-team competition can serve
as a catalyst for intra-team cooperation by stimulating improvements in
relative group performance. Chen (2008) further highlighted the intricate
interplay between cooperation and competition in real-world scenarios. In the
realm of AI and computer science, there exists a substantial body of
literature dedicated to investigating the dynamics of cooperation and
competition among AI models or agents within simulated environments (Dafoe et
al., 2020; Drury et al., 2009). Castelfranchi (1998) introduced a framework
for modeling social environments using AI agents, encompassing elements such
as cooperation, groups, and organizations. Building upon this foundation,
Dafoe et al. (2020) and Zhang et al. (2023) proposed open problems in
cooperation AI, aiming to enhance the joint welfare of a group, thereby
contributing to the understanding of these dynamics in artificial
environments. In our work, we leverage LLM-based agents, known for their
profound natural language understanding and reasoning capabilities, to
effectively stimulate human behaviors and probe the intricate interactions
within a novel competition scenario. Our contribution bridges a significant
gap in the literature by addressing the under-explored dimension of
competition in AI and computer science. Moreover, our work distinguishes
itself from recent contributions such as Chen et al. (2023b) by introducing a
versatile competition framework adaptable to diverse competitive situations.

####  5.2.1 Social Learning Theory and Market Competition Theory

Social learning theory (Bandura and Walters, 1977) posits that individuals
learn new behaviors through observation, imitation, and modeling. This
paradigm has found applications in various domains such as psychology,
education, and sociology (Latham and Saari, 1979; Deaton, 2015; Davis and
Luthans, 1980). It serves as a robust framework for comprehending the
intricate interplay between individual cognition and external influences in
learning.

In this work, we explore the application of social learning theory by
scrutinizing the behaviors of LLM-based agents in an interactive environment.
Through comprehensive experiments, we successfully elucidate how LLM-based
agents exhibit efficient social learning behaviors, and establish their
potential utility in simulating complex dynamics in various disciplines social
science.

Market competition theory (Smith, 1937) elaborates on how companies and
organizations compete for consumer attention and finite resources within a
marketplace (Smith, 1937), which plays a vital role in understanding economic
dynamics, shaping business strategies, and informing public policy decisions
(Hirshleifer, 1978).

In this study, we delve into the applicability of market competition theory by
investigating how competition among LLM-based agents influences their learning
processes and decision-making mechanisms. Through meticulous empirical study,
we have found compelling evidence that when these agents engage in competitive
environments, they exhibit substantial enhancements in service quality and
capacity to adapt their strategies to meet the diverse and evolving needs of
their customers. These findings underscore the importance of embracing
customer-centric strategies to achieve success in competitive market
landscapes.

##  6 Limitations and Future Directions

While this study offers a valuable initial exploration of LLM-based agents in
a competition scenario, it should be considered a stepping stone for more
comprehensive research in this domain. Specifically:

  * •

Sample Size and Diversity. Due to the constraints imposed by the GPT-4 API
limitations, our experiments did not involve a significant number of
restaurants and customers. We see this not as a hindrance but as an
opportunity for future research to build upon our work by expanding the number
of interacting agents to gain insights into more complex consumer behavioral
studies and business decision-making.

  * •

Text-Based Interactions. Our current framework leverages GPT-4, the most adept
text-based Language Learning Model (LLM), which predominantly relies on
textual data. We acknowledge that real-world environments often involve multi-
modal interactions and inputs, such as image, video, and audio. As more
sophisticated multi-modal LLMs become publicly available on a large scale, we
anticipate that future studies could offer a more holistic view.

  * •

Version-Specific Findings. The results in this paper are based on GPT-4
version gpt-4-0613. We acknowledge that future API updates may affect the
results. To facilitate direct comparisons, we recommend utilizing the same API
version. Meanwhile, we encourage researchers to adapt and enhance LLM-based
frameworks to accommodate the rapid advancements in language model
technologies, ensuring the continued relevance and applicability of their
findings.

These limitations not only outline the boundaries of the present study but
also pave the way for targeted, in-depth research that could further enrich
our understanding of machine learning applications in the context of
restaurant-customer interactions.

##  7 Conclusion

We introduced a general framework called CompeteAI to study the dynamics of
competition using large language model-based agents. By instantiating the
framework as a virtual town with restaurant and customer agents, we
extensively explored the competition behaviors of agents. Our study revealed
several interesting findings in accordance with classic sociological and
economic theories. To conclude, our work confirmed that LLM-based agents can
be used for simulating the competition environment, providing research
experience for future studies on sociology, economics, and human study.

## Disclaimer

We leveraged LLM-based agents to generate plans for running a restaurant or
writing a comment. Our study does not output any irresponsible or risky words.

## References

  * Abdelnabi et al. (2023) Sahar Abdelnabi, Amr Gomaa, Sarath Sivaprasad, Lea Schönherr, and Mario Fritz.  Llm-deliberation: Evaluating llms with interactive multi-agent negotiation games.  _arXiv preprint arXiv:2309.17234_ , 2023.
  * Ahn (2002) Sanghoon Ahn.  Competition, innovation and productivity growth: a review of theory and evidence.  _Available at SSRN 318059_ , 2002.
  * Akata et al. (2023) Elif Akata, Lion Schulz, Julian Coda-Forno, Seong Joon Oh, Matthias Bethge, and Eric Schulz.  Playing repeated games with large language models.  _arXiv preprint arXiv:2305.16867_ , 2023.
  * Argyle et al. (2023) Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate.  Out of one, many: Using language models to simulate human samples.  _Political Analysis_ , 31(3):337–351, 2023.
  * Bandura and Walters (1977) Albert Bandura and Richard H Walters.  _Social learning theory_ , volume 1.  Englewood cliffs Prentice Hall, 1977.
  * Bol et al. (2018) Thijs Bol, Mathijs De Vaan, and Arnout van de Rijt.  The matthew effect in science funding.  _Proceedings of the National Academy of Sciences_ , 115(19):4887–4890, 2018.
  * Buşoniu et al. (2010) Lucian Buşoniu, Robert Babuška, and Bart De Schutter.  Multi-agent reinforcement learning: An overview.  _Innovations in multi-agent systems and applications-1_ , pages 183–221, 2010.
  * Castelfranchi (1998) Cristiano Castelfranchi.  Modelling social action for ai agents.  _Artificial intelligence_ , 103(1-2):157–182, 1998.
  * Chan et al. (2023) Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu.  Chateval: Towards better llm-based evaluators through multi-agent debate.  _arXiv preprint arXiv:2308.07201_ , 2023.
  * Chang et al. (2023) Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Kaijie Zhu, Hao Chen, Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al.  A survey on evaluation of large language models.  _arXiv preprint arXiv:2307.03109_ , 2023.
  * Chen et al. (2023a) Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, and Rui Yan.  Semi-offline reinforcement learning for optimized text generation.  In _ICML_ , 2023a.
  * Chen et al. (2023b) Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, and Kyle Richardson.  Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auction arena.  _arXiv preprint arXiv:2310.05746_ , 2023b.
  * Chen (2008) Ming-Jer Chen.  Reconceptualizing the competition—cooperation relationship: A transparadox perspective.  _Journal of management inquiry_ , 17(4):288–304, 2008.
  * Dafoe et al. (2020) Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R McKee, Joel Z Leibo, Kate Larson, and Thore Graepel.  Open problems in cooperative ai.  _arXiv preprint arXiv:2012.08630_ , 2020.
  * Davis and Luthans (1980) Tim RV Davis and Fred Luthans.  A social learning approach to organizational behavior.  _Academy of Management review_ , 5(2):281–290, 1980.
  * Deaton (2015) Shannon Deaton.  Social learning theory in the age of social media: Implications for educational practitioners.  _Journal of Educational Technology_ , 12(1):1–6, 2015.
  * Deutsch (2011) Morton Deutsch.  Cooperation and competition.  _Conflict, interdependence, and justice: The intellectual legacy of Morton Deutsch_ , pages 23–40, 2011.
  * Drogoul et al. (2002) Alexis Drogoul, Diane Vanbergue, and Thomas Meurisse.  Multi-agent based simulation: Where are the agents?  In _International Workshop on Multi-Agent Systems and Agent-Based Simulation_ , pages 1–15. Springer, 2002.
  * Drury et al. (2009) John Drury, Chris Cocking, Steve Reicher, Andy Burton, Damian Schofield, Andrew Hardwick, Danielle Graham, and Paul Langston.  Cooperation versus competition in a mass emergency evacuation: A new laboratory simulation and a new theoretical model.  _Behavior research methods_ , 41(3):957–970, 2009.
  * Ferber and Weiss (1999) Jacques Ferber and Gerhard Weiss.  _Multi-agent systems: an introduction to distributed artificial intelligence_ , volume 1.  Addison-wesley Reading, 1999.
  * Gabriel and Ghazavi (2021) Iason Gabriel and Vafa Ghazavi.  The challenge of value alignment: From fairer algorithms to ai safety.  _arXiv preprint arXiv:2101.06060_ , 2021.
  * Gao et al. (2023) Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, and Yong Li.  S3: Social-network simulation system with large language model-empowered agents.  _arXiv preprint arXiv:2307.14984_ , 2023.
  * Hardy et al. (2023) Mathew Hardy, Ilia Sucholutsky, Bill Thompson, and Tom Griffiths.  Large language models meet cognitive science: Llms as tools, models, and participants.  In _Proceedings of the annual meeting of the cognitive science society_ , volume 45, 2023.
  * Hillman et al. (2009) Amy J Hillman, Michael C Withers, and Brian J Collins.  Resource dependence theory: A review.  _Journal of management_ , 35(6):1404–1427, 2009.
  * Hirshleifer (1978) Jack Hirshleifer.  Competition, cooperation, and conflict in economics and biology.  _The American Economic Review_ , 68(2):238–243, 1978.
  * Horton (2023) John J Horton.  Large language models as simulated economic agents: What can we learn from homo silicus?  Technical report, National Bureau of Economic Research, 2023.
  * Huang et al. (2023) Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec.  Benchmarking large language models as ai research agents.  _arXiv preprint arXiv:2310.03302_ , 2023.
  * Jansen et al. (2023) Bernard J Jansen, Soon-gyo Jung, and Joni Salminen.  Employing large language models in survey research.  _Natural Language Processing Journal_ , 4:100020, 2023.
  * Jin et al. (2023) Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, and Srijan Kumar.  Better to ask in english: Cross-lingual evaluation of large language models for healthcare queries, 2023.
  * Khanna et al. (1998) Tarun Khanna, Ranjay Gulati, and Nitin Nohria.  The dynamics of learning alliances: Competition, cooperation, and relative scope.  _Strategic management journal_ , 19(3):193–210, 1998.
  * Kosfeld and Von Siemens (2011) Michael Kosfeld and Ferdinand A Von Siemens.  Competition, cooperation, and corporate culture.  _The RAND Journal of Economics_ , 42(1):23–43, 2011.
  * Latham and Saari (1979) Gary P Latham and Lise M Saari.  Application of social-learning theory to training supervisors through behavioral modeling.  _Journal of Applied Psychology_ , 64(3):239, 1979.
  * Li et al. (2023a) Cheng Li, Jindong Wang, Kaijie Zhu, Yixuan Zhang, Wenxin Hou, Jianxun Lian, and Xing Xie.  Emotionprompt: Leveraging psychology for large language models enhancement via emotional stimulus.  _arXiv:2307.11760_ , 2023a.
  * Li et al. (2023b) Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem.  Camel: Communicative agents for "mind" exploration of large scale language model society.  _arXiv:2303.17760_ , 2023b.
  * Li et al. (2023c) Yuan Li, Yixuan Zhang, and Lichao Sun.  Metaagents: Simulating interactions of human behaviors for llm-based task-oriented coordination via collaborative generative agents.  _arXiv preprint arXiv:2310.06500_ , 2023c.
  * Liu et al. (2023a) Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M Dai, Diyi Yang, and Soroush Vosoughi.  Training socially aligned language models in simulated human society.  _arXiv preprint arXiv:2305.16960_ , 2023a.
  * Liu et al. (2023b) Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al.  Agentbench: Evaluating llms as agents.  _arXiv preprint arXiv:2308.03688_ , 2023b.
  * Luo et al. (2022) Meifeng Luo, Fuying Chen, and Jiantong Zhang.  Relationships among port competition, cooperation and competitiveness: A literature review.  _Transport Policy_ , 118:1–9, 2022.
  * Markussen et al. (2014) Thomas Markussen, Ernesto Reuben, and Jean-Robert Tyran.  Competition, cooperation and collective choice.  _The Economic Journal_ , 124(574):F163–F195, 2014.
  * OpenAI (2023) OpenAI.  Gpt-4 technical report, 2023.
  * Panait and Luke (2005) Liviu Panait and Sean Luke.  Cooperative multi-agent learning: The state of the art.  _Autonomous agents and multi-agent systems_ , 11:387–434, 2005.
  * Park et al. (2023) Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein.  Generative agents: Interactive simulacra of human behavior.  _arXiv preprint arXiv:2304.03442_ , 2023.
  * Porter (2008) Michael E Porter.  _On competition_.  Harvard Business Press, 2008.
  * Rigney (2010) Daniel Rigney.  _The Matthew effect: How advantage begets further advantage_.  Columbia University Press, 2010.
  * Salop (1979) Steven C Salop.  Monopolistic competition with outside goods.  _The Bell Journal of Economics_ , pages 141–156, 1979.
  * Smith (1937) Adam Smith.  _The wealth of nations [1776]_ , volume 11937.  na, 1937.
  * Song et al. (2023) Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su.  Llm-planner: Few-shot grounded planning for embodied agents with large language models.  In _Proceedings of the IEEE/CVF International Conference on Computer Vision_ , pages 2998–3009, 2023.
  * Stapel and Koomen (2005) Diederik A Stapel and Willem Koomen.  Competition, cooperation, and the effects of others on me.  _Journal of personality and social psychology_ , 88(6):1029, 2005.
  * Törnberg et al. (2023) Petter Törnberg, Diliara Valeeva, Justus Uitermark, and Christopher Bail.  Simulating social media using large language models to evaluate alternative news feed algorithms.  _arXiv preprint arXiv:2310.05984_ , 2023.
  * Touvron et al. (2023) Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al.  Llama: Open and efficient foundation language models.  _arXiv:2302.13971_ , 2023.
  * Walberg and Tsai (1983) Herbert J Walberg and Shiow-Ling Tsai.  Matthew effects in education.  _American educational research Journal_ , 20(3):359–373, 1983.
  * Wang et al. (2023a) Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar.  Voyager: An open-ended embodied agent with large language models.  _arXiv preprint arXiv:2305.16291_ , 2023a.
  * Wang et al. (2023b) Jindong Wang, HU Xixu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Wei Ye, Haojun Huang, Xiubo Geng, et al.  On the robustness of chatgpt: An adversarial and out-of-distribution perspective.  In _ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models_ , 2023b.
  * Wang et al. (2023c) Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al.  A survey on large language model based autonomous agents.  _arXiv preprint arXiv:2308.11432_ , 2023c.
  * Wang et al. (2023d) Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang.  Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents.  _arXiv preprint arXiv:2302.01560_ , 2023d.
  * Wu et al. (2023) Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang.  Autogen: Enabling next-gen llm applications via multi-agent conversation framework.  _arXiv preprint arXiv:2308.08155_ , 2023.
  * Xi et al. (2023) Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al.  The rise and potential of large language model based agents: A survey.  _arXiv preprint arXiv:2309.07864_ , 2023.
  * Xiao et al. (2023) Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, et al.  Large language models can be good privacy protection learners.  _arXiv:2310.02469_ , 2023.
  * Zeng et al. (2023) Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang.  GLM-130b: An open bilingual pre-trained model.  In _ICLR_ , 2023.  URL https://openreview.net/forum?id=-Aw0rrrPUF.
  * Zhang et al. (2023) Ceyao Zhang, Kaijie Yang, Siyi Hu, Zihao Wang, Guanghe Li, Yihang Sun, Cheng Zhang, Zhaowei Zhang, Anji Liu, Song-Chun Zhu, et al.  Proagent: Building proactive cooperative ai with large language models.  _arXiv:2308.11339_ , 2023.
  * Zhao et al. (2023) Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang.  Expel: Llm agents are experiential learners.  _arXiv preprint arXiv:2308.10144_ , 2023.
  * Zhou et al. (2023) Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury.  Synthetic lies: Understanding ai-generated misinformation and evaluating algorithmic and human solutions.  In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ , pages 1–20, 2023.  doi: 10.1145/3544548.3581318.
  * Ziems et al. (2023) Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang.  Can large language models transform computational social science?  _arXiv preprint arXiv:2305.03514_ , 2023.