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Automated Negotiation: Can Machines Replace Human Negotiators?
Leslie SHAW
Associate Professor
ESCP Europe
79 avenue de la République, 75011 PARIS
+33637347809
Tibo NOEL
Junior Consultant
Ginkgo Management Consultants GmbH
Lehmweg 17 20251 Hamburg, Deutschland
+491711141382
Benjamin SPICER
EU M&A General Execution
Morgan Stanley
25 Cabot Square, Canary Wharf
London E14 4QA England
+442074258000
Automated Negotiation: Can Machines Replace Human Negotiators?
Abstract
This paper describes what automated negotiation is, gives an
overview of its development, explores the benefits it can bring
to B2B transactions and examines the challenges and limitations
involved in replacing human negotiators by machines.
Keywords
automated negotiation
Résumé
Cette communication décrit la négociation automatisée, donne un
aperçu de son développement, explore les avantages qu'elle peut
apporter aux transactions B2B et examine les défis et les
limites liés au remplacement des négociateurs humains par des
machines.
Mots clés
négociation automatisée
Foreword
Negotiators exchange messages, process and evaluate data, make
concessions, impose conditions, make tradeoffs, accept or
reject proposals, make counter-proposals, decide whether to
close a deal or walk away.
Machines can perform all of these tasks better than human
agents. Computers are not prone to misunderstanding and do not
lie, bluff, threaten or lose their tempers.
Take e-bay’s automated bidding system. Bidder A can bid $10 for
an item and set a maximum amount he is ready to pay, say $50.
As other bidders make higher offers, the system will trump them
incrementally up to the maximum set by Bidder A, who will
either get the item at the optimal price (one increment above
the highest competing bid) or abandon the bidding process once
the maximum amount is reached.
Such efficiency would be impossible in a traditional auction,
where emotion might prompt a bidder to pay more for an item
than originally intended or back off in the face of an
aggressive competitor.
While e-commerce, albeit of a primitive kind (the sole variable
is price) has become widespread in B2C and C2C transactions, it
does not appear to have made the same inroads in B2B
negotiations as in other aspects of business (catalogue,
inventory and procurement management) although it is
technically possible to automate B2B negotiations for variables
other than price, such as discount, delivery time, incoterms,
payment terms, warranty and maintenance. There are only two
differences between our e-bay example and a B2B scenario: in
the former, there is one variable (price) and only one party
makes concessions (the bidder). In automated B2B negotiations,
both sides need to make concessions and there are multiple
variables.
If we compare human and automated B2B negotiations, the main
differences are that the former use natural language, include
messages that are ambiguous or irrelevant to the object of the
negotiation and rely on subjective evaluations and partial
decisions, whereas the latter use machine language, focus
exclusively on the variables at hand and make objective
evaluations and impartial decisions.
Academics and others who write about negotiation tend to deal
only with human negotiation and steer clear of automated
negotiation, which is the preserve of mathematicians, experts
in artificial intelligence and game theorists. An Amazon search
(October 2013) gives 88 results for “B2B Negotiation” and 2
results for “Automated B2B Negotiation”. While much work has
been done on the subject of automated negotiation, it is often
abstract, inaccessible to the layman and not directly
applicable in terms of deployment of the technology in B2B
negotiations.
Despite the existence of a global e-commerce infrastructure
that could support automated negotiation, most B2B negotiations
are still conducted by human agents in the same way they have
been for centuries: through face-to-face discussions.
This paper describes what automated negotiation is, gives an
overview of its development, explores the benefits it can bring
to B2B transactions and examines the challenges and limitations
involved in replacing human negotiators by machines.
Introduction
Developments in artificial intelligence can transform the
buying process as we know it through the use of software agent
technologies. A software agent is an entity governed by
computational algorithms and operates according to a series of
objectives set by human users. It can make critical decisions
in real time and substitute for a user with limited input from
the latter. It can run continuously and explore the purchasing
environment for an indefinite amount of time. Take a shipping
company that needs to buy varying amounts of differently sized
(and priced) boxes, in a periodic, yet irregular, fashion. To
manage this process the company programs a software agent to
monitor inventory and deploy a purchasing agent when stock
reaches critical levels. The purchasing agent scans the market,
gathers data on suppliers and products, assesses product
quality and suitability, decides which products are most
compatible with the company’s needs, negotiates price and
executes payment. Such technologies offer unprecedented
opportunities to transform traditional negotiation processes.
In this paper we shall explore the methods, aims and potential
of automated negotiation.
Automated Negotiation
In 1999 Maes, Gutman, & Moukas published Agents That Buy and Sell,
an introduction to the functionalities and future potential of
intelligent agents. They assert that “the potential of the internet for
transforming commerce is largely unrealized. Electronic purchases are still largely
non-automated. While information about products and vendors is more easily
accessible, and orders and payments are dealt with electronically, humans are still in
the loop in all stages of the buying process, adding to transaction costs.”1
In this pioneering article, a contribution on automated
negotiation was written by Tuomas Sandholm, who explains that
automated negotiation is nothing more than the process of
computational agents who find and prepare contracts on behalf
of real life parties. Although the term automated negotiation
was coined before the release of Sandholm’s article, he was one
of the first to list concrete fields of application and clear
advantages of the technology.2 Research jumped on the topic and
rebranded automated negotiation from a futuristic vision to a
promising technology (Skylogiannis, 2005). Today, automated
negotiation is an umbrella concept for Negotiation Support Systems,
Genetic Algorithms or Electronic Negotiations.
Negotiation as a Search Problem
Negotiation is a distributed search through a space of potential agreements
(Jennings et al, 2001). Such space can be represented as a
geometric shape with multiple dimensions, each representing a
variable to be negotiated. Negotiators move across the space
until they locate a single point acceptable to all, or the
process is aborted. Each party has one or more regions of
1 Maes, Gutman & Moukas (1999), p. 81.2 For earlier use of the term 'automated negotiation' see Rosenschein & Zlotkin (1994).
acceptability, geometric portions of the search space which contain
all the points representing acceptable positions. They also
have regions of preference, sub-portions of the regions of
acceptability containing preferred settlements. The negotiation
space as a whole, and each participant's regions of
acceptability in particular, are not immutable but change in
real time. When a new variable is introduced, an additional
dimension is added to the system. When a variable is dropped,
the spatial representation of the process decreases by one
dimension. Spatial regions of preference and acceptability can
morph to reflect the inclusion of new, previously unacceptable
points or the exclusion of previously acceptable combinations.
Automation of the Search Problem
Defining negotiation as a geometric search problem allows us to
establish an argument in favor of process automation. To solve
the problem, negotiators must explore a large amount of
geometric space. The search proceeds iteratively: one
participant suggests a point, to which others respond
positively or negatively. In the former case the search is
over, in the latter another point is suggested. This process is
repeated until a mutually suitable solution is found, or the
negotiation is abandoned. For a given, potentially large
negotiation space, the number of points to explore in order to
find a solution is high and the number of such attempts
increases exponentially along with the number of variables.
Each new variable adds a dimension to the spatial
representation of the negotiation. From a scientific point of
view, the academic field that has developed the most in-depth
research on solving search problems is computer science.
Traditionally, computer science search issues have a practical
aspect and arise from the need to locate information objects
within large databases. Admittedly, such an application domain
differs from the negotiation problem. In negotiation, the
search problem does not concern data bases, but is mathematical
in nature: participants need to scan and locate dimensions
identified by mathematical properties within a conceptual model
of space as opposed to a physical database. Notwithstanding
this difference, computer science offers compelling methods to
solve the search problem, notably a large number of search
algorithms. An algorithm is a set of consecutive steps which
describe the behavior of an object within a system. Starting
from an initial static state, an algorithm describes a set of
instructions that initiate a waterfall process eventually
resulting in a predefined output (Knuth, 1973). It can be
argued that algorithms are more suited to address negotiation
search problems than people are, since they possess the
following properties:
1. Speed. For a mathematical instruction, a computer is orders
of magnitude faster in execution than a human being. The
iterative, linear and deterministic nature of a search
algorithm allows it to process large sets of data seamlessly
and rapidly. Its distributed nature further increases the
computational power available to solve the negotiation.
2. Memory. A computer system running a search algorithm embeds a
data storage repository, such as a hard drive. We have seen
in the search metaphor described above that each
negotiator’s region of preference and acceptability may vary
during the process. An algorithm can not only adapt to such
new search requirements, but access all points previously
considered. Thus, an algorithm can recall previously
discarded offers if the inhibiting factors behind their
rejection cease to exist. While in theory a human negotiator
could do the same, he displays far greater cognitive
restrictions than a machine.
3. Adaptive Learning. Computerized agents can employ a wide array of
mathematical and statistical learning tools that can analyze
large data sets, extract descriptive patterns and improve
their behavior without explicitly being programmed to do so
(Samuel, 1959). The chief benefit these instruments bring to
the negotiation table is a better ability to understand and
predict the behavior of negotiation counterparts.
4. Evidence Based. Buyers regard of selection between competing
products as a zero-sum choice. But irrational behavior often
plays a role in bargaining decisions. Negotiators can find
it difficult to separate personal issues with their
counterparts from negotiation-relevant data and hence risk
making sub-optimal decisions. Computerized negotiating
systems offer users the certainty that any outcome would be
generated by the users’ scale of preferences alone. In other
words, while automated negotiation won’t remove the
possibility of buyer’s remorse, it can help ensure that any
potential remorse is due to the user’s initial scale of
preferences, and not to behavior oversights and biases.
5. Soft-Skill Agnostic. Negotiation is a field where interpersonal
capabilities dominate the skill spectrum. Common sense
recognizes that veteran negotiators will have an advantage
over persons with less experience. By delegating the process
to machines, we level the playing field, since search
algorithms interact with each other with the exclusive
objective of maximizing utility. Notwithstanding this
principle, we can envision a future where technological
power replaces soft-skill efficacy: in competitive
negotiations, advanced algorithms might prove more effective
than others.
6. Resource-Light. Negotiation is resource-intensive and requires
time to prepare and execute. Depending on the context,
effective negotiations may require support from dedicated
professionals. Related costs increase exponentially along
with the dimensions and complexity of the variables. By
contrast, the popularity of open-source software is booming,
and cheap hardware is ubiquitous. Machines can, therefore,
provide users with an economically sound method of
conducting negotiations.
Autonomous Software Agents
We have just observed that computerized systems show great
potential to improve the negotiation process and that
algorithms address many of the issues faced by human
negotiators. How can such solutions be deployed, by whom, and
in what context? Before answering these questions, we must look
at the subject of autonomous agents, the cornerstone needed to
understand the challenges of automated negotiation. Autonomous
agents are software entities that act on behalf of a human
owner to reach his objectives (Jennings, 2001). They deploy
algorithmic technologies to seek an optimal solution to the
negotiation search problem. Here are some examples to
illustrate what agents are capable of doing:
1. Identifying the Optimal Choice. A buyer wants to book airline
tickets. He chooses the destination, dates, airport of
departure and the maximum fare he is willing to pay. He
transfers this information to his software agent, which
scans the marketplace, selects combinations matching the
buyer’s preferences and submits this information to the
buyer. Following the search process, the agent can either
find a combination which is compatible with the required
preferences or not. In the latter case, the agent can use
statistical means to determine the likelihood of finding
suitable offers in the next few days. If the likelihood is
low, the agent can begin searching for alternative options.
For example, the agent can initiate a search for flights
leaving from a different airport, on different dates, to
different destinations, at different fares. Each criterion
can be changed depending on market conditions and the
existence of matching options. At the end of the process,
the agent collates the different search results and returns
to the buyer with the data and an explanation for the
alternative behavior.
2. Cross-Market Negotiations. A buyer wants to purchase a machine.
He establishes a set of specifications and stipulates that
while he requires the agent to search for a suitable deal,
he will approve the final decision. The agent trawls the
marketplace and compiles a report stating a reasonable price
for the desired specifications. This information is used to
drive the purchasing strategy. Using a baseline price
calculated by taking into account prices from the dealers,
the agent initiates a negotiation process with sellers it
deems likely to reduce price, such as drop-ship sellers or
small dealers. The agent can also scour online auction
websites to find cheaper alternatives. Once the agent
compiles a list of options, he submits it to the buyer, who
makes a final choice based on the information provided. The
agent then executes the payment and agrees on delivery
terms.
3. Cooperation. A supplier gets an order from a retailer that
exceeds his capacity to meet. The order is for 600 units
per day, capacity is 400. Since the retailer is a key
customer, the supplier decides to search for alternative
solutions which would allow him to accept the order. His
agent contacts other suppliers with a request for
collaboration. If agents representing those suppliers
respond with a manifestation of interest, a formal
negotiation process is initiated to decide appropriate terms
for the collaboration. When a suitable arrangement is found,
the agent closes the deal and reports the details back to
the supplier.
The Design of Automated Negotiation: Protocol and Strategy
Automated negotiation is two or more agents seeking to reach an
agreement. Two dimensions characterize the system in which they
perform these activities: protocol and negotiation strategy
(Skylogiannis, 2005). Protocol is a set of rules that governs
the players’ interaction and ultimately defines the players’
behavior. This protocol is transparent, open and public. Some
examples of negotiation protocols are:
- Participants: who can negotiate?
- Valid or invalid actions: which message can be sent to
whom and when?
- Negotiation status: is a proposal accepted or rejected?
Because of their impact on process, behavior and outcomes,
these rules of encounter are a dominant concern when designing
an automated negotiation system (Jennings & Faratin, 2001).
Automated negotiation strategy, on the other hand, is
confidential and sometimes hidden. When addressing this
dimension, researchers look at how participants develop ways to
achieve their best personal outcome. Naturally, these
strategies have to abide by the rules set in the protocol.
Nonetheless, the strategies can diverge greatly from one
another in terms of when and what to concede, when to accept
and how to communicate with others (Bartolini, Preist, & Kuno,
2002).
In a supply chain scenario a logistics agent would focus on
obtaining small orders and would trade off order timing in
reaching this objective. A sales agent, on the other hand, with
customer satisfaction an absolute priority, will be more
willing to accept other terms as long as delivery dates are
respected.
With protocol and strategy defined, the two main design
elements of an automated negotiation system are covered. The
following section explores the topics related to automated
negotiation and shows how vast the research field is and how
these topics interrelate.
FIGURE 1: AUTOMATED NEGOTIATION AND RESEARCH STREAMS
To comprehend automated negotiation it is crucial to understand
negotiation as a concept and intelligent agents. They form the
prerequisites or input elements of automated negotiation
research. As discussed, protocol and strategy both have a major
impact on the outcome of a negotiation. Current research on
automated negotiation can be divided into a theoretical
mathematical stream and a computational or practical stream,
with the latter gaining importance and popularity. These two
streams can be considered as the output of research on
automated negotiation.
When observing the related topics of automated negotiation,
they can be arranged according to their degree of human
involvement and automation. The advent of the Semantic Web (a
web of data that can be processed by machines) should allow for
more inter-operability in all fields of IT and as such be a
driving factor towards more automation. Sandholm (1999) speaks
of a simultaneous ‘technology push’ and ‘application pull’ by
the self-interested software entities representing various
stakeholders.
FIGURE 2: SIMILAR TOPICS & SEMANTIC WEB AS A DRIVER OF
AUTOMATION
The Future of Automated Negotiation
We shall now discuss the challenges, limitations and future of
the technology in order to complement the more technical and
academic viewpoints covered thus far.
Advantages
According to Dr. Tuomas Sandholm, the main advantage of the
technology is that it saves time for negotiating entities. He
argues that intelligent agents who prepare and exchange
contracts on behalf of real life parties do so at very high
speed. In addition, Sandholm mentions the ability of these
computational agents to find deals in combinatorial and
strategically complex settings. Lastly, he argues that
negotiation is often more than a constant-sum bargaining over
price. Due to these different preferences of the parties,
automated negotiation can find solutions which improve utility
for all parties, thus creating a win-win situation [see
Sandholm’s contribution in Maes, Gutman, & Moukas, (1999)].
Luo et al. (2003) argue for several more advantages of
automated negotiation. They mention that the lower transaction
costs of automated negotiation allow for more frequent
negotiations, among more partners, over lower value variables.3
In addition, they state that automation removes human emotions
or sensibilities (desire for power, feelings of embarrassment,3 Linked to this higher frequency is the potential for automatic negotiationto allow ordinary users to perform like experts in complicated issues (Das,Hanson, Kephart, & Tesauro, 2001). Their vision is that through automation,anyone can negotiate about anything!
revenge or humiliation) that could lead to sub-optimal
outcomes. A further advantage is that automated negotiation
does not require participants to be co-located in space or
time. This in turn increases the potential number of
negotiating entities, which augments the possibility of finding
a suitable negotiation partner and obtaining an optimal
outcome.
These advantages can be regrouped by adopting a macro-view on
the subject. It is important to note that the advantages stated
in research so far are always relative - in comparison to
traditional negotiation - and often normative or idealistic. In
other words, the advantages are only valid under the assumption
that the technology is fully operational (no software bugs) and
efficient (reduced transaction costs). The technology has to be
in an ideal state for these advantages to hold weight.
FIGURE 3: ADVANTAGES OF AUTOMATED NEGOTIATION CATEGORIZED
As depicted in Figure 6, the advantages cited in the research
of Sandholm (1999), Luo et al (2003) and Das et al. (2001) have
been categorized into three groups. Although quite general,
these categories show that the potential benefits of automated
negotiation are very broad and enable us to better contrast the
benefits of the technology with traditional negotiation.
Challenges and Limitations
A first major drawback of automated negotiation is the lack of
a widely accepted framework for the technology. This was
already pointed out in the work of Jennings & Faratin (2001)
and although attempts have subsequently been made to fill this
void, our research has not identified an existing dominant
reference.4 Not only does a framework provide a common lens for
future research, it also allows a better comparison of existing
articles. Furthermore, a common framework can help speed up the
development and implementation of the technology in the real
world. Similarly, ontology 5 was addressed by Nwana et al. in
1998 as a major challenge for automated negotiation. Early
researchers were well aware of the need for standards for this
technology to be widely adopted.
Secondly, a lack of practical case studies, examples and
implementation experience remains a concern for researchers.
Jennings & Faratin (2001) mention the need for the development
of a best practice repository for automated negotiation
techniques. They imagine this to be a coherent resource that
would state which negotiation technique or agent is best suited
to a given problem or domain. Key findings and lessons learned
from other references are inevitable for the diffusion of a new
technology. Organizations and people tend to be risk averse
when trying out new technologies and want to see concrete proof
before adoption. The lack of case studies, implementation
experience and best practices is a major obstacle for the
widespread adoption of the technology. That being said, one
4 For attempts to build a framework see amongst others Lomuscio, Wooldridge & Jennings (2003), Kersten & Lai (2007) and Lopes, Wooldridge & Novais (2008).5 Ontology is the classification of research objects and their interrelation.
could argue that a successful implementation of the technology
could be viewed as a competitive advantage and as such would
probably be kept confidential. In a way this creates a vicious
circle that is hard to escape from. Companies want more proof
before using the technology, yet the technology cannot further
evolve as long as companies do not share their experience of
using it.
In Nwana et al. (1998), the authors mention security, privacy
and communication as serious challenges and potential
obstacles. Today, we can observe that no standards for these
three aspects exist. Nonetheless, efforts are being made in
this direction, as the Semantic Web demonstrates. As for
security, an article in The Economist on February 23, 2013 shows
that this topic is very much alive and as such, also relevant
to automated negotiation technology:
“Foreign governments and companies have long suspected
that the Chinese hackers besieging their networks have
links to the country’s armed forces. On February 19th
Mandiant, an American security company, offered evidence
that this is indeed so. (…) Mandiant claims that hackers
at Unit 61398 have stolen technology blueprints,
negotiating strategies and manufacturing processes from
more than 100, mainly American, companies in a score of
industries.”6
6 “China’s cyber-hacking - Getting Ugly”, The Economist, 23rd February 2013.
It is clear that when intelligent agents are employed, they
will be running in a network of computers and servers, with
their subsequent settings and parameters. Without claiming that
this is what is implied by the term ‘stealing negotiating
strategies’ as written in the article, it is clear that these
settings and parameters can be the object of interest for third
parties. Knowledge is power, especially when it comes to
knowing your negotiating opponent. Indeed, the use of
intelligent agents and computers to conduct negotiations on
behalf of human counterparts brings with it higher exposure and
risk from hackers infiltrating systems and stealing
confidential information.
Lastly, the question of when automation is applicable is raised
by several researchers (Gimpel, 2006). According to Gimpel not
every negotiation can be transformed into an automated
negotiation. A high degree of certainty of the negotiator’s
objectives, preferences and tactics is required (Interneg,
2007). The more structured and robust a negotiation context is,
the easier it would be to automate it. Indeed, Braun &
Brzostowski (2006) propose having some well defined steps such
as information seeking and presenting offers being outsourced
to agents, while ambiguous issues such as changing protocol, or
decisions regarding the relationship between parties are better
left to the human principals.
Following this approach, the design of agents for a supply
chain within the same organization (with a common corporate
goal and clear instructions) would require less effort than the
design for agents for different organizations (with different
goals, strategies and tactics). This view is shared by An
(2011), who points out that information uncertainty of any kind
as well as market dynamics make it difficult to develop
successful negotiating agents. Nonetheless, uncertainty can be
managed by creating a climate of trust. Nick Jennings (2005)
writes in his Building Automated Negotiators that obtaining the
user’s trust to adopt and implement the technology is one of
the major obstacles for automated negotiation. Blecherman
(1999) stated in his research that using a computer to conduct
negotiations is ultimately a voluntary decision by all the
parties, and unless each party has reason to do this, it will
be unsuccessful. Every party needs to underwrite the benefits
of using the technology.
The technology of automated negotiation has not been perfected
yet and even if it were, certain disadvantages are likely to
persist. The lack of a common framework for theoretical and
research purposes, and the lack of best practices and case
studies for practical implementation are main obstacles to
wider adoption by organizations.
Evolution of and Prospects for Automated Negotiation
We have demonstrated that automated negotiation has come a long
way. As a sub domain of Artificial Intelligence, automated
negotiation itself has been the driver of new and related
research. In the 1990s, with the rise of the personal computer
and the Internet, the quest for digitalization had only just
started. Researchers had only just begun to develop
mathematically sophisticated computer models and were more
interested in the question “what can computers do for us?” In the
field of automated negotiation, research was primarily focused
on how agents can represent human objectives and interests in
an efficient way. How can they make commitments on behalf of
the human owner, and thus maximize his utility? (Jennings,
2005). The focus lay on the autonomy of the agents. It is
clear, however, that in any negotiation context, communication,
bargaining and exchange of proposals are crucial.
Two autonomous agents can each calculate their best proposal,
but if they cannot communicate or alter their position in
function of the other party (in other words, negotiate), they
will be unsuccessful. At the end of the day, reaching an
agreement is what negotiating is all about. This is why
currently the focus lies on having ‘intelligent’ agents and not
only ‘autonomous’ agents. The interaction instead of merely acting
with other entities (software, agents, and machines) is what
the future of automated negotiation looks like (An, 2011). This
shifting research view of automated negotiation is illustrated
in Figure 7. It should be noted that this shift in views is not
absolute and traceable to a specific year or publication.
Rather, earlier achievements in terms of agent autonomy and the
tendency of agents to operate in an increasingly connected
world will make the domain of interaction the focus of future
research.
FIGURE 4: PAST & FUTURE: RESEARCH FOCUS OF AUTOMATED
NEGOTIATION
Another important facet of the future of automated negotiation
concerns so-called ‘learning agents’. Because of the limited
knowledge agents sometimes have both of their counterparts as
well as on their dynamic environment, it is important for an
agent to be able to update its beliefs and strategies based on
interaction with others. Researchers have used so-called genetic
algorithms for agents to learn the best strategies to reach
their objectives, and to learn about the preferences of their
counterparts.7 It is likely that learning agents will become
steadily more important in future research.
Hybrid methods are likely to become more widespread in the coming
years. In these models, different negotiation methods
(traditional negotiation, NSS and automated negotiation) are
combined or integrated, resulting in a set of tools to address
specific negotiation issues. In essence, hybrid models aim to
create synergies by integrating NSS and automated negotiation
(Rehman, 2008). What is more, they aim to fill the existing
void in human agent negotiations. In a way, they could be
called ‘semi-automated negotiation’, because they blend both
agents and NSS elements. We believe in the rise of hybrid
models in the coming years because of their easy applicability
and proven track record.
An interesting viewpoint on this topic is proposed by He,
Jennings, & Leung (2003). They mention the rise of virtual
enterprises, which are agile and fluid partnerships between
companies that share resources and skills to complete a
particular project or finish a certain product.8 These non-
fixed networks of companies are made possible by the rise of
ICT, which facilitates the opportunity of finding partners
worldwide. Greater efficiency and specialization, a larger
(common) offering, and more flexibility are cited as the main
advantages. Automated negotiation plays a central role in this
7 For detailed research on learning agents see Zeng & Sycara (1998), Coehoorn & Jennings (2004), Lau (2005).8 For a detailed study on Virtual Enterprises, see Martinez, Fouletier, Park& Favrel (2001).
vision, because these partnerships are negotiated on a
continuous basis by agents of the system: any agent within the
network will accept to take over a task from another agent in
the network as long as its marginal benefits are higher than
its marginal costs (Sandholm, 2000).
Lastly, a study by Baarslag, Jonker, Hindriks, Kraus, & Lin
(2010) speaks of an entire new function for automated
negotiation. They suggest that agents could be used to properly
train human negotiators before they actually perform their
tasks. The proposal evaluation and information retrieval phases
are the most likely focus of such training sessions. After all,
the computational resources of the agent are immense and human
negotiators can learn how the agent would respond or react in
any given situation. This function of automated negotiation is
examined in a working paper by Lin, Oshrat, & Kraus (2009).
As previously mentioned, automated negotiation is still a
largely unexplored terrain from an empirical perspective. Very
few case studies and real life examples exist. However, with
the elements covered in this research so far, a wide range of
future potential applications spring to mind. Such applications
include utilities markets, bandwidth allocation, manufacturing,
distributed vehicle routing among independent dispatch centers,
and electronic trading of financial instruments (Maes, Gutman,
& Moukas, 1999). Taking into account the structural condition
and robustness of the negotiation context, we believe the
greatest potential lies in the area of supply chains and
procurement. Large conglomerates, which have hundreds of
internal production processes, could hugely benefit from the
gain in efficiency (higher speed and lower transaction costs)
of not having to deal with every negotiation in a traditional
way.
With an overview of the potential of automated negotiation
technology, we are now sufficiently armed to discover some
empirical results of ‘man vs. machine’ studies.
Man vs. Machine: Who Scores Best?
Before addressing this question, it is important to note that
every empirical study conducted to date has focused on one
particular domain of negotiation. From bilateral negotiations
over price (single issue) to multilateral negotiations over a
multitude of issues, many studies compare the performance of
agents with human negotiators.
Obviously, the notion of performance is interpreted differently
by each of these studies. Are we talking about the best results
for the buying side? Optimal results for the selling side?
Pareto efficiency? The lens through which these comparative
studies view the notion of performance plays an important role
in reaching a conclusion.
Secondly, negotiation context, dynamics and protocol have a
huge impact on the performance of agents and people. For
instance, should the timing and speed of the negotiations be
considered a Key Performance Indicator in assessing people or
agents? All of these questions and variables leave room for
debate and make it difficult to formulate a general answer to
the question on who performs best. A number of detailed and
recent studies focus on this question. Some of them are very
positive on the performance of agents whereas others do not
detect any noticeable difference in performance. This balanced
view should allow for a better understanding of the ‘man vs.
machine’ debate in the field of negotiation.
One group of studies involves people negotiating directly
against an intelligent agent. They are interesting because the
agent must deal with different personalities and negotiation
styles. Accordingly, the agent must be able to rely on a strong
opponent modeling component in order to be successful (Oshrat,
Lin, & Kraus, 2009). In their research, Oshrat, Lin, & Kraus
(2009) conclude that their agent (named KBAgent) achieved
significantly higher values than the human players. The values
they base their conclusions on is the utility that a negotiating
party can obtain in the context of a job interview.9 Although
in a specific context, the fact that the agent consistently
outperformed human negotiators remains impressive. A similar
outcome was found in a study by Chari & Agrawal (2007), where
they indicate that agents can perform as effectively as human9 For a detailed description of the experiment, see Oshrat, Lin, & Kraus (2009), p 6. With issues such as (a) salary, (b) job description, (c) social benefits, (d) promotion and (e) working hours, 1296 possible agreements existed in the experiment, and the negotiators had a maximum of 28 minutes to conclude.
negotiators in a given setting. The authors even state that
agents can act as surrogates of human negotiators under certain
circumstances. A third study by Jim R. Oliver (1997) underlines
the capacity of agents to effectively replace people in e-
commerce negotiations.
On the other hand, Bosse and Jonker (2008) conducted
experiments to compare the performance of software agents
against people in multi-issue negotiations. Their results did
not indicate that agents performed better than experienced
human negotiators. An important remark is that whereas
automated agents might be more efficient in maximizing utility
when negotiating with another agent, they might score poorly
when interacting with people. (Lin & Kraus, 2008).
Valuable research into the performance of automated negotiation
is the organization of ‘automated negotiation competitions’.
During these competitions, the design and strategies of
automated negotiation agents are evaluated in a tournament
setting. In 2010 in Toronto, seven agents representing five
universities from around the world competed in the first
automated negotiating agents competition (abbreviated as ANAC).
After successful tournaments in 2011 and 2012, the fourth
edition was held in Saint Paul, Minnesota in May 2013 (ANAC,
2013). The focus of this tournament was the learning and
adaptation capabilities of agents. The official goals of the
competition were:10
10 To read the detailed rules of encounter of the competition see www.itolab.nitech.ac.jp/ANAC2013
1. To encourage the design of practical negotiation agents
that can proficiently negotiate against unknown opponents
and in a variety of circumstances.
2. To explore different learning and adaptation strategies
and opponent models.
3. To collect state-of-the-art negotiating agents and
negotiation scenarios, and making them available to the
wider research community.
We strongly believe in the importance of such competitions to
increase overall research transparency and to accelerate the
adoption of the technology in business applications. A general
statement on who (agent or human being) performs better remains
difficult if not impossible to make. More comparative studies
in this domain are required to obtain a classification of when
and where agents can outperform human negotiators.
Conclusion
We have seen how, thanks to advances in artificial
intelligence, agent technology is currently at its prime.
Recent breakthroughs in the field of artificial intelligence
and machine learning have disrupted the negotiation landscape,
and we can only marvel at what these technologies could achieve
in the next years. Automated negotiation technologies appear to
be an attractive proposition for a variety of reasons. As
previously explained, they have been used to assist customers
throughout the shopping experience, to support and inform
sellers, and to improve B2B processes such as supply chain
management and logistics. However, while the technology aspects
of automated negotiation are improving exponentially, we are
unsure that the same can be said for its real-life
applications. It is true that, on the one hand, we have
provided examples of how these methods have been applied to the
academic and corporate world, both in B2C and B2B contexts. On
the other hand, however, it would seem that the impact of
automated negotiation on real-world business processes has
remained largely unexpressed. Indeed, automated negotiation
technology has been available for the best part of the last 15
years, yet it is difficult to find information on household
name companies employing them successfully. Moreover,
information on the subject is limited to its technical
specification, and the existing literature is mainly produced
by experts in the fields of computer science and engineering.
It is also a very sectorial subject, and one is left to wonder
on how many C-level executives in large companies are aware of
what their firms could achieve through automated negotiation.
The field seems to suffer from an information asymmetry
problem: business scholars and decision makers can truly mark
the adoption of these technologies, yet they appear to remain
in the dark about their existence and potential. Computer
scientists, in the meantime, seem to focus on issues of
engineering and cosmetics, rather than on practical
implementation. Automated negotiation requires a new generation
of business scholars able to bridge the current gap and develop
common, interdisciplinary ground. The current generation of
digitally-native, computer-literate and internet-savvy business
students and practitioners has, in our opinion, such potential.
