SEE EMERGENCE AS A METAKNOWLEDGE

A Way to Reify Emergent Phenomena in Multiagent Simulations?

Daniel David and R

emy Courdier

EA2525 - LIM - IREMIA, University of La R

eunion

2 rue Joseph Wetzel, Sainte-Clotilde, R

eunion, France

Keywords:

Emergence, Simulation, Multiagent Systems, Metaknowledge.

Abstract:

Emergence is a fascinating concept for most scientists, and multiagent simulations are known to allow and

facilitate its representation. Research in this area yield to several deﬁnitions and classiﬁcations of emergent

phenomena, but only a few of them offers a solution for a concrete reiﬁcation of emergence in simulation.

This paper deals with this important notion of emergence reiﬁcation that, as we know, does not have yet formal

mathematic deﬁnition, if any could be expressed. We need to progress on the conceptual meaning, leading to

more global deﬁnitions but allowing to give a general conceptual framework that makes possible the reiﬁcation

of emergent phenomena in multiagent simulations. We deﬁne emergence as being a metaknowledge and we

present a conceptual framework in which emergent phenomena can be detected and injected into simulation

systems and be handled like other entities.

1 INTRODUCTION

Simulation of natural and social systems is a cross-

disciplinary activity in which computer scientists

work with many other researchers who are the experts

of their ﬁelds (geography, biology, economics, etc.).

These experts, that we call thematicians, deﬁne the

models that lie at the core of any simulation: they in-

put the properties and functional descriptions of the

system entities. Their goal is to use computer simu-

lations to imitate operations of real-world processes

or systems over time (Banks, 2000) and then improve

the collective knowledge about the system. They are

the owners of the knowledge on the models, and for

them computer science is simply seen as a tool.

However, most of them have to face the question

of emergence during the design of simulation mod-

els. Early deﬁned by greek philosophers with the

now-famous phrase “the Whole is more than its Parts”

(Palmer, 2000), the emergence concept is present in

most scientiﬁc ﬁelds. Many are the real-world ex-

amples that thematicians try to understand and ex-

plain: collective behavior among ants colonies, reg-

ulation of stock markets, ﬂocking of birds, etc. In this

context, the scientists try to ﬁnd answers to a series

of recurring questions (Di Marzo Serugendo et al.,

2006; Fromm, 2005), including: What does emerge?

What are the properties of the phenomenon that has

emerged? or Can we understand the emergence of

this phenomenon? that are all linked with the most

general one: What is emergence?

This question, which arises for real-world phe-

nomena, also arises for the virtual simulations we cre-

ate to imitate them. That is why working on the con-

cept of emergence, its representation and its integra-

tion in simulations is therefore essential to improve

our understanding (i) of the simulation itself and (ii)

of the real phenomena that are represented.

Emergence is so an extensively studied concept

in the complex systems ﬁeld. Many work has been

done in software engineering (Hu et al., 2007; Abbott,

2007) and many deﬁnitions and classiﬁcations have

been proposed by the multiagent community (Deguet

et al., 2007). Multiagent Simulation (MAS) constitute

for emergence a very good expressing place when-

ever emergent phenomena are known to be unilat-

eral or bilateral (Castelfranchi, 1998), weak or strong

(Dessalles et al., 2007), synchronic or diachronic

(Stephan, 2002), intrinsic or causal (Boschetti and

Gray, 2007). Thus, MAS are particularly suited for

the emergence concept. They allow us to discover and

highlight the phenomena that emerge in the system.

A good and simple example is the one of ant-

agents who try to link their nest to a food source (Dro-

goul and Ferber, 1994). The ants have a very simple

behavior but after a while all ant-agents follow the

564

David D. and Courdier R. (2009).

SEE EMERGENCE AS A METAKNOWLEDGE - A Way to Reify Emergent Phenomena in Multiagent Simulations?.

In Proceedings of the International Conference on Agents and Artiﬁcial Intelligence, pages 564-569

DOI: 10.5220/0001803305640569

 SciTePress

shortest path between their nest and the food source.

This shortest path has “emerged” from the collective

behavior of agents and its existence is due to the sys-

tem consideration as a whole.

But emergence should not be considered as a

simple observation result and emergent phenomena

should have a real place in simulation. For exam-

ple, if we want to simulate a lagoon and its ﬁshes,

it would be better to detect potential shoals of hun-

dred ﬁshes that can be formed, and then being able

to inject them into the system instead of any of the

hundred ﬁshes belonging to the shoals. That is called

emergence reiﬁcation. There would be there a sig-

niﬁcant interest for the system comprehension and in

terms of complexity drop during the simulation. But

for computer scientists emergence is often manipu-

lated thanks to their knowing of simulation platforms

and speciﬁc concepts while for thematicians it is often

seen only as a result.

According to this, we propose in this paper a ﬁrst

step toward a formalism which is useful but for which

we still need progress. In the next section, we will

make a key proposition by deﬁning emergence as a

metaknowledge. Then, we will take advantage of

this deﬁnition to propose a conceptual framework in

which emergent phenomena can be reiﬁed through

emergent structures. Within this framework, emer-

gent phenomena can be detected and injected in the

simulation system through mechanisms of introspec-

tion and intercession to then be manipulated like any

other entity.

2 EMERGENCE AS

METAKNOWLEDGE

Attempting to reach a possible reiﬁcation of emergent

phenomena, we focused on a concept that appears in

many scientiﬁc ﬁelds related to the design of systems

and to artiﬁcial intelligence: metaknowledge (Pitrat,

1990; Paguette, 1998; Kalfoglou et al., 2000).

We found in literature that metaknowledge is

linked with many philosophical concepts or theories

which are very close to our point of view about emer-

gence, like meronomy or mereology, research ﬁelds

that deal with relations between a Whole and its Parts

(Keet, 2006). And when we talk about Parts and

Whole, emergence is never far away. In the exam-

ple “the rum of this punch”, the punch is more than

the sum of its parts (rum, sugar, fruits, etc.), because

these ones can not be separated since the punch was

created (and irreducibility is a characteristics of some

emergent phenomena (Stephan, 2002)). This encour-

aged us in order to ﬁnd a link between emergence and

metaknowledge.

Metaknowledge is a tool to work on knowledge

and it has been deﬁned as “knowledge on knowledge

rather than knowledge about a particular area such

as mathematics, medicine or geology” (Pitrat, 1990).

The concept is very broad but it can be reﬁned so we

can consider among others:

• the metaknowledge describing knowledge;

• the metaknowledge on the use of knowledge;

• the metaknowledge to discover knowledge;

• the metaknowledge to manipulate knowledge.

Thus, seeing metaknowledge as a tool to discover, de-

scribe, utilize and manipulate knowledge is in full ﬁt

with the fundamental concept of emergence, includ-

ing the ones of radical novelty (knowledge discovery)

and of interdependence levels (use and manipulation

of knowledge) (Stephan, 2002). According to this, in

our approach we consider the following deﬁnition.

Deﬁnition. Emergence is a metaknowledge.

This is a key proposition of this paper. Metaknowl-

edge is at the heart of the process of transforming

information into knowledge. It offers a greater vari-

ety of attitudes and a better way to adapt changes oc-

curring in environments (Luzeaux, 1997) and in that

sense this concept is particularly suited to the study

of emergence in MAS. So this deﬁnition of emer-

gence as a metaknowledge enables us to have new

approaches for simulation modeling in order to take

into account the emergence of phenomena as best as

possible.

3 CONCEPTUAL FRAMEWORK

FOR EMERGENCE

REIFICATION

3.1 Modeling Approach

To reify emergent phenomena that do occur, we need

to built knowledge and metaknowledge on the simu-

lation, even if we do not forget that metaknowledge is

only a supplementary knowledge: the difference lies

in the levels of abstraction that can be obtained for

example through a subjective external observer.

We know that MAS provides knowledge on studied

systems, and it is the study of this knowledge that will

provide us metaknowledge on the initial systems. For

the sake of genericity, this proposal takes place in a

MAS model that is most general as possible. We con-

ceive that agents are entities acting through mecha-

nisms of perception, inﬂuence and interaction within

SEE EMERGENCE AS A METAKNOWLEDGE - A Way to Reify Emergent Phenomena in Multiagent Simulations?

565

one or more environments. The proposal is at a con-

ceptual level that allows us not to take into account

the speciﬁc concepts that are integrated and handled

the different multiagent simulation platforms.

As shown on Figure 1, the idea is to construct the

knowledge about the MAS by using mechanisms of

observation on the simulation. This will provide us

the useful elements about the agents, their environ-

ment(s), and their evolutions. The metaknowledge

about the MAS, and so the emergent phenomena that

may occur in simulation, is then deﬁned using the el-

ements of the constructed knowledge. That means

that all the emergent phenomena will be described us-

ing combinations of whatever happened (or may hap-

pen, or... may have happen) in the simulation sys-

tem. Moreover, and by deﬁnition of metaknowledge,

when some phenomena emerge, their belonging to the

metaknowledge about the MAS means that they be-

long to the knowledge about it too, and of course to

make sense they have to be add to the MAS itself.

Figure 1: From the MAS to the metaknowledge.

This modeling approach makes possible to themati-

cians to think about the simulated systems at more

abstract levels. Indeed, emergent properties cannot be

expressed with the same level used to deﬁne the sys-

tem entities. For example, it is not possible to express

the notion of shortest path in the simulation of ants

colonies moving to a source of food with the same

parameters used to deﬁne ants behaviors.

Moreover, it offers the possibility for thematicians to

inject into a simulation the properties, behaviors and

patterns that emerged. In order to make this concep-

tual framework intent to detect and learn emergent

properties of the simulated systems, we propose in

the followings a ﬁrst step toward a useful formalism

of these two aspects of emergence reiﬁcation. Draw-

ing on works found in philosophy and about reﬂection

and metaobjects (Kiczales, 1991), we called them in-

trospection and intercession processes.

3.2 Introspection Process

In philosophy, introspection is a method of observa-

tion and analysis to study one’s own person and to

acquire self-knowledge. To yield to the detection of

emergent phenomena, the MAS has to do its own in-

trospection. Obviously, this process is based on the

analysis of the facts that are happening in the simu-

lation. So in the context of our study, we deﬁne the

introspection process as follows.

Deﬁnition. The introspection process consists in

creating and expressing knowledge and metaknowl-

edge on the simulation in order to detect phenomena

that do emerge.

To detect the emergent phenomena that appear in a

simulation, we need to know the set (or a set) of all

the facts that are occurring (or have occured in the

case of a post-simulation analysis) in the MAS. As

we said before, these facts are related to the agents

behavior and to their environment. They are function

of space, time, communication, and, more generally,

they depend on the different entities and the different

metrics present in the MAS.

Using the observation mechanisms offered by

simulation platforms (probes, publish/subscribe

mechanism) to observe the interactions of the agents

and the evolutions of the environment(s) in which

they evolve, we can build K , a set deﬁned as follows:

K = { f acts}

This set represents the useful knowledge (for emer-

gent phenomena analysis) that can be studied on the

MAS. Moreover, we can reﬁne K as follows :

K = K

∪ K

where K

, which gathers the facts produced by

agents, is deﬁned by:

= {in f luences, perceptions,interactions}

and where K

is the set of all the facts produced

within the environment.

The emergent phenomena are deﬁned through the

study of the set of facts K that represents the knowl-

edge on the system. Such an analysis of K consists to

establish relations between facts. This helps to high-

light the changes that occur in the simulation and that

ICAART 2009 - International Conference on Agents and Artificial Intelligence

566

represent emergent phenomena. To detect these po-

tential changes, we propose to deﬁne an extensible set

of functions R

deﬁned as follows:

= { f : K

→ boolean}, n ∈ N

Each function of this set detects if combinations of

parameter facts deﬁne an emergent phenomenon. We

deﬁne these functions as emergence revelators.

Using these emergence revelators, we can now de-

ﬁne the P

set of emergent phenomena:

= { f ∈ R

/ f = true}

It is important to notice that these mechanisms of

knowledge construction and analysis do not limit

themselves to simple conjunctions of facts: time,

space, or communication events can be considered to-

gether, as any other types of facts, to reveal complex

emergence situations. For example, back to the in-

trinsic emergence (as deﬁned in (Boschetti and Gray,

2007)) example of the lagoon and its ﬁshes, we can

consider that the emergent phenomenon “shoal of

ﬁshes” only occurs when hundred ﬁshes are in the

same neighborhood, during a minimum period, and

when the ﬁsh agents interact to know in which di-

rection they should evolve. Thus, our approach is

very powerful because we give the possibility to de-

tect emergent phenomena that occurs in complex sit-

uations mixing informations on space, time, commu-

nication, or whatever.

3.3 Intercession Process

Once emergent phenomena have been detected in

simulations, we need to “give them life”. In our con-

ceptual framework, this is done during the interces-

sion process that we deﬁne as follows.

Deﬁnition. The intercession process consists in the

injection into the MAS of the emergent phenomena

that have been detected during the introspection

process.

Most of the time, emergent phenomena that occur in

the real world are at least materialized or character-

ized. Their existence impact directly on other enti-

ties of the real world. Some of them may take part

of the emergent phenomena, others are inﬂuenced by

these phenomena, and others have their perception of

world modiﬁed from the presence of these phenom-

ena. We decided to set up such mechanisms in the

virtual world of simulations. Thus, the detection of

emergent phenomena sometimes yields to the deﬁ-

nition of entities that will directly inﬂuence agents

behaviors in the MAS. These entities manifest them-

selves through different kind of elements that we de-

ﬁne hereafter : emergence agents and interposition

elements managed by emergent metastructures with

which they constitute emergent structures.

An emergence agent is an agent that runs on a

MAS platform. It thus evolves in the same environ-

ment as all other agents of the system and interacts

with them through the mechanisms of inﬂuence and

perception offered by the platform. Several emer-

gence agents can be created to reify the same phe-

nomenon.

An interposition element is a modiﬁcation of one

or several environments. It changes (as appropriate by

altering, improving, restricting, etc.) the perception

or inﬂuence mechanisms associated with one or more

agents.

These two elements are controlled by emergent

metastructures that we call ms

, which are them-

selves governed by laws of emergence. These emer-

gence laws are all the elements of the set L

deﬁned

as follows:

= { f : P

→ S

}, n ∈ N

where S

represents the set of all the emergent struc-

tures.

An emergent structure is deﬁned by a tuple

<emergent metastructure, emergence agents, interpo-

sition elements>. Each emergent structure is man-

aged (created, modiﬁed, deleted) by its own emergent

metastructure.

An emergent phenomenon p

∈ P

is materialized

in the MAS through an emergent structure s

∈ S

in function of the laws deﬁned in L

. Depending of

its properties, this emergent phenomenon p

is con-

cretely injected inside the simulation as a one or more

emergence agents, as one or more interposition ele-

ments, or as a combination of both.

The independent or complementary use of emer-

gence agents and interposition elements allow us to

take into account different types of emergence that

could occur in MAS. Thus, in the shoal of ﬁsh ex-

ample, the shoal will be represented directly in the

system by an emergence agent. The ﬁshes that con-

stitute the shoal of ﬁshes may continue to evolve in

their environment, but will have their inﬂuences and

perceptions changed by elements of interposition con-

trolled by the ms

corresponding to the shoal of ﬁsh.

Here, as soon as the thematicians are able to observe

and then characterize the behavior of the shoal of ﬁsh,

it is possible to keep it into the system and to remove

each one of the hundred ﬁsh-agents that may consti-

tute it. At least we can imagine that the ﬁshes that

evolve in the shoal will not need to exchange mes-

sages to ﬁnd collectively the best direction for mov-

SEE EMERGENCE AS A METAKNOWLEDGE - A Way to Reify Emergent Phenomena in Multiagent Simulations?

567

Figure 2: Complete framework for emergence reiﬁcation.

ing, but that the shoal will take this decision for all of

them. No more interactions to deal with for hundred

agents : the complexity drop is important.

Notice that this approach supports the dynamism

of emergent phenomena, especially their volatility:

when an emergent phenomenon is no longer ob-

served, its corresponding emergent structures are

deleted from the MAS. Indeed, the functions in P

(the ones that activated the corresponding laws in L

)

are no longer deﬁned.

3.4 Emergence Reiﬁcation

As said in introduction, emergence should have a bet-

ter place in the design cycle of models and simula-

tions. Figure 2 shows a summary of our proposal,

with both the “inside simulation” aspect and the con-

ceptual vision for the complete cycle of emergence

reiﬁcation that we deﬁne as follows.

Deﬁnition. The emergence reiﬁcation consists in

doing the complete cycle of the introspection and

intercession processes

We can also see on Figure 2 that because emergent

structures can have their own behavior, they also con-

tribute (via their own inﬂuences and perceptions) to

create new knowledge that can be added to the set of

facts. Once more, this contributes to reﬁne the knowl-

edge on the MAS.

At the junction of the conceptual works and the

MAS representation, we identiﬁed three services that

simulation platforms should have to reach such a reiﬁ-

cation of emergent phenomena:

• observation services (to set up the observation

mechanisms useful to build knowledge on the

MAS);

• an agent manipulation service (to create, delete

and modify agents cycle of life);

• an environment manipulation service (to manipu-

late the interposition elements).

Notice that the MAS platform that we develop in our

lab ﬁts with the conceptual framework we deﬁned in

this paper. This argues for the feasibility of our ap-

proach. Because of the genericity of the proposal, it

is a reuse advantage that would beneﬁt to other simu-

lation platforms that would like to set up such emer-

gence reiﬁcation mechanisms.

Before concluding, it is important to remind that

with this conceptual framework, thematicians have a

new role to play during the modeling cycle of simu-

lations: thinking about the R

and L

sets that ap-

pears in the processes of introspection and interces-

sion. This is consistent with what happen in the real

world: thematicians do not have any innate knowl-

edge on emergent phenomena and it is only from ob-

servation that they have learn to recognize, character-

ize or name them. The ﬁrst phenomena that emerge

and that may be detected by mechanisms of observa-

tion are the result of the descriptions initially provided

by the thematicians. So forth, the detected emergent

phenomena will constitute a kind of new knowledge,

that it is important to add to the global knowledge of

the system. This will yield to a better understanding

ICAART 2009 - International Conference on Agents and Artificial Intelligence

568

of the simulation system itself and to the discovery

of new emergent phenomena that could not have been

identiﬁed with the thematicians initial knowledge.

4 CONCLUSIONS & DISCUSSION

In this paper, we focused on the emergence issue and

on its representation in MAS. We consider that we

need to improve the way this concept is taken into ac-

count in simulations and we proposed a conceptual

framework that enables the reiﬁcation of emergence

in simulations.

Actually, this is allowed by the analysis of the

knowledge on the simulation. That is why the main

issue of our approach is the deﬁnition of emergence

as a metaknowledge on the MAS: it is the key con-

cept that we used to propose and describe a concep-

tual framework for the detection and injection of the

different kinds of emergent phenomena. Thanks to

this, we identiﬁed the services that should be avail-

able in simulation platforms to take emergence into

account.

Our experience on large-scale simulation projects

and our long-time wondering about the representation

of emergence in MAS (Marcenac et al., 1998) has led

us to make this conceptual framework the most sim-

ple and generic as possible. In that sense, we consider

it as a ﬁrst step (i) toward a formalism which is use-

ful but for which we still need progress and (ii) in

the way of designing models and programming with

emergence.

In future works, we will use the conceptual frame-

work we described in this paper to improve a multia-

gent application of energy simulation under develop-

ment on our simulation platform GEAMAS-NG, in

the context of a research program ﬁnanced by the Re-

union Island. Drawing on this conceptual framework

we will also extend our platform in order to take emer-

gence into account as soon as we start the conception

of the simulation agents, while keeping a clear sepa-

ration between initial behaviors and emergent ones.

This would improve agents capacities by giving to

them the possibility of reasoning on themselves and

so on emergent phenomena that will get back some

piece of magic.

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