Monitor for Digital EcosystemWP4.5

2nd Tech. Meeting, Girona 2-3-4/04/07

Speaker: Peplluis de la Rosa

Contract no. 03744

Monitor for Digital Ecosystems Self-Organization and Emergent Properties of Agent Interaction Communities: A Bio-inspired Approach

Measuring Ecosystem Properties

Developing a tool for studying and characterising ecosystem features, like heterogeneity, openness, stability, security and fitness of the population of the intelligent agents (one for each SME user), that impact on the distributed learning process. Monitoring of ecosystem characteristics will guarantee: equal opportunity, efficiency, effectiveness, trust and worth, and respect of private property.

Monitor for Digital Ecosystems

4.5 Measuring Ecosystem Properties The monitor of the ecosystem is a tool aimed at understanding and predicting the

stability properties of the ecosystem with the final aim of improving the ecosystem performance. In this task we will implement a tool (monitoring system) to measure characteristic properties such as: Openness, Dynamics, Homogeneity/Diversity, Exploitation/Exploration, Trust and worth (linked with WP5), Security (linked with WP5). The tool will have a user interface allowing the negotiator to assess the state of the ecosystem with respect to the mentioned characteristics. These will be monitored during the execution of the Ecosystem (the DBE) by random samples of contracts and interactions through time, and will give hints about how to regulate the types of population (types of intelligent agents and their recommendation strategy) in the ONE ecosystem. Because of required considerations of trust and privacy, it is not possible to execute this level of analysis on an ecosystem-wide basis and it is proposed that participants would be encouraged via incentive to “opt-in” to such a scheme. An incentive to participate in such an analysis mechanism could be that only those who are willing to share their information would receive the benefits of the analysis.

From the Technical Annex (TA)

Monitor for Digital Ecosystem WP4.5

PhilosophyA monitor of an ecosystem as a feedback system

Meta Recommender

Business Ecosystem (Model?)

Actuation

ONE Eco-Monitor

Reference

INDEX• Philosophy• ONE EcoMonitor Components

– 1. Model of Interaction– 2. Properties of Ecosystem / Business– 3. Monitor Equations– 4. Styles of Negotiation / Relation– 5. Meta Recommendation

• Properties Proposal• Negotiation and Relational Styles Proposal• Example Scenarios• Public and private information to feed the ecosystem

monitor

Monitor for Digital Ecosystem WP4.5

A Possible Interaction Model - Lotka Volterra

• y is the number of some predator (for example, wolves); • x is the number of its prey (for example, rabbits); • dy/dt and dx/dt represents the growth of the two populations against time; • t represents the time; and • α, β, γ and δ are parameters representing the interaction of the two species.

Idea

Analysis and Action

Idea – Another Model – Strange Attractor Lorenz

ONE Eco-Monitor Components

1. A Simple Interaction Model: GAS ModelRemark: the model of business ecosystem is the MODEL, while the model of a ecosystem is the MODEL of the MODEL, and while the simple interaction model of gas is the MODEL of the MODEL of the MODEL. This is a meta2 model (sorry).

Agents can move about the community lattice by selecting a direction c in which to move. Formally, an agent at lattice site r moves to a different site r + c, which must be vacant, i.e. n(r+c,t) = 0 giving,

The lattice site r + c is one of the eight neighboring sites of r; or r itself if c = 0. The algorithm used by agents to select the new direction c is presented below.

The agents mutually exclude each other, i.e. at most one agent per site. When updating the locations of agents, there is the possibility that two or more agents will be moved to the same site. To deal with this problem the agents move asynchronously. The agent that moves onto the site first wins. This agent is selected at random from those competing for the site. The total amount of community interaction received by the community member at site r by τ (r, t) is recorded. The measure of the total interaction time τ (r , t) is registered in the same units as the discrete time step. When an agent is present at a site, it interacts with the community there, giving

ONE Eco-Monitor Components

The field τ (r, t) and in particular its fluctuations are the main focus of this work. A relatively smooth τ (r, t) reflects evenly distributed community interaction, while a wildly fluctuating τ (r, t) represents haphazard community-interaction.Before formulating the boundary conditions for the model is necessary to specify exactly what is internal and external to the system. The system is only that part of the agent system containing the community and the community-interaction agents. The agents in the rest of the system are regarded as an external pool (or reservoir) of potential community elements. This is bio-inspired by the observation of that ant workers spend 50–75% of their time inactive and that this serves as a pool of labour for the ant colony.

ONE Eco-Monitor Components

Agents move onto the community from the external pool via an external driving rate p: The community is commonly located away from the limits of the system. Therefore, agents are able to enter the community from any side. Consequently, each boundary site receives a new agent with equal probability p every time step, i.e.

This external driving rate is a measure of the number of agents in the community. The more agents in the external pool (i.e. the non-community system), the more can move onto the community, and the greater is p: The model could study the correlation between p and the equilibrium density of agents actually on the community lattice, so that very low p values could referred as sparsely populated environments and high p as densely populated ones.

ONE Eco-Monitor Components

The model adopts open boundary conditions. Any agent moving onto a boundary lattice site is deemed to have left the community and is removed from the system, i.e.

where rb denotes a boundary lattice site.

ONE Eco-Monitor Components

Coop-Services

Cellular automata simulator

ONE Eco-Monitor Components

Potential vendor(transport)

Potential vendor(security)

Potential vendor(cleaning)

Potential vendor(catering)

ONE Eco-Monitor Components

Opens a negotiation processfor a service (a congress).

ONE Eco-Monitor Components

The proximity of the nodes is based on their compilance of the requirements stated on

the tender.

e.g.• Negotiation type• Services required• Availability• …

ONE Eco-Monitor Components

A negotiation process occurs. It can be between

two or more nodes.

ONE Eco-Monitor Components

The environment readapts itself, based on the new

environmental conditions.

No transportagreement

No securityneeded

Not clear aboutcatering needs(doesn’t move)

ONE Eco-Monitor Components

The monitor records interactions between nodes.

These interactions give information for defining the situation of the different nodes in order to determine their role in the ecosystem and its global state.

Also it records negotiation types and results for future case studies.

Meta Recommender

ONE Eco-Monitor Components

2. Properties of Ecosystems / Business• Irreducible models• Interacting Factors• Feedback Mechanisms• Resilience• Hierarchy• Regulation Mechanisms• Stochastic• Holistic system-understanding, no reductionism• Self-organisation and emergent properties (bottom-up and top-down effects)• Freedom of action• Contingency• Heterogeneity• Evolution towards increasing complexity• Overall structure becomes a representation of all the information received• Complexity represents a synthesis of the information in communication

ONE Eco-Monitor Components

Six basic orientors or requirements to develop a system:1. Existence. Must not exhibit any conditions which may move

the state variables out of its safe range2. Efficiency. The exergy gained should exceed the exergy

expenditure3. Freedom of Action. Reacts to the inputs with variability4. Security. Has to cope with the threats5. Adaptability. If can not cope with threats, must change the

system itself6. Consideration of other Systems. Must respond to the behaviour

of other systems

We’ll focus on the following propertiesDiversity competition, offer or demandStability stability framework, predictability, securityResilience invisible hand, free market

Decision Making

Ecosystem

Ecosystem Monitor

Meta Recommendation

Human Decisions

FunctionEvaluation

Information

Diversity (Shannon index)

Measure of diversity (local or spatially defined area)Values typically range from 1.5 to 3.5

ONE Eco-Monitor Components3. Monitor Equations for Measuring the Properties

Local-Global Information (Fisher index)

H (Shannon index) is said to be a global measure of the behavior of p(x). Fisher index (I) is thereby sensitive to local rearrangement of interactions, it is said to have a property of locality. Thus H is a global measure, while I is a local measure, of the behaviour of the curve p(x).

Organisational Complexity

The alternative measures for representing organisational complexity in agent communities have been evaluated and considered for their suitability to ONE. The Physical complexity measure was then chosen for further investigation, as its properties closely matched our intuitive definition of organisational complexity.

Physical complexity is derived from the notion of conditional complexity defined by Kolmogorov, which is different from traditional Kolmogorov complexity. It states that the determination of complexity is conditional on the environment in which is interpreted. The complexity C of a population of agents, where l (linking) is the maximal entropy of the population minus the sum over the link l of the per-community entropies H(i).I =

Resources Contents Efficiency

Let x1; . . . ; xn0 be the densities of all the interaction stages. The total interaction supply Θj for an individual of the jth stage is calculated as the quotient of the consumed resources to its own resource content. This has the form:

Here, ej > 0 is the external interaction supply, the term pijxi determines the interact supply of the ith stage. The coefficients pij; 1 ≤_ i ≤_ n0; 1 ≤_ j ≤_ n0, are constant, nonnegative, and pij = 0 if fij = 0. If fij > 0 and pij = 0, then we ignore the positive effect for the jth stage caused by interacting stage i. Each pij is calculated by:

where Ci and Cj are the resource contents of the individual agents from the corresponding stages.

DiversityA diverse biological system is more likely to be dynamically stable than one that is not diverse because in diverse communities interactions may often play a larger role in a species' success.

To the extent that changes in the system are driven by interactions, it is dynamically stable, since characteristics of the system itself are determining its future state.

There are some problems with this formulation of the diversity-stability hypothesis, however. It verges on circularity. The larger (more diverse) the system considered, the fewer are the things left out of it. The fewer the things left out, the smaller the possible outside influences on the system. The smaller the possible outside influences, the greater the degree of dynamic stability. Thus, dynamic stability is (almost) a necessary consequence of diversity, simply because diverse systems include many components.

Proposal of Ecosystem Properties

Diversity – StabilityAlthough it is a key question, the relationship between diversity and stability is still being resolved. As with many topics in biodiversity, there are different ways of expressing stability. One way is to define it as the ability of a system to return to its original state after being disturbed, so how quickly it can return and how large a disturbance it can return from are key variables. Another definition is how resistant to change the system is in the first place. No matter what the definition

used, however, there are definite trends that appear.

Proposal of Ecosystem Properties

Does Diversity lead to Stability? If either the redundancy or rivet theories are correct, then more species means more stability. Current consensus is that greater diversity does lead to greater stability, for three general reasons: Insurance Effect: Different species do better under different conditions. As the number of species increases, the range of conditions that at least some species do well in also increases. When perturbations do occur, it is more likely that some of the species present will be able to do well, and these species will protect the community as a whole. Averaging Effect: Stability is measured as variability relative to community abundance. As diversity increases, the value of the variability will naturally decrease. One problem with this is that the impact of additional species can be confused with the effect of larger numbers of individuals. Negative Covariance Effect: Since species are competing for resources, any gains that one species makes will be to some extent at the expense of the other. This means that as a species does more poorly its competitors will do better. The result is that disturbances are not as detrimental to the entire system as they could be, as the losses in one species are offset by the gains of another.

Proposal of Ecosystem Properties

There are at least three ways in which stability might be defined. • Constancy - The ability of a community to resist changes in composition and

abundance in response to disturbance. Not a particularly useful concept of stability because few, if any, ecosystems could be described as constant.

• Resiliency - The ability of a community to return to its pre-disturbance characteristics after changes induced by a disturbance. Resiliency corresponds to stability the way it is studied in mathematical models. It measures a system's tendency to return to a single stable point, but many ecological systems appear to have multiple stable points. If disturbance remains below a particular threshold, it will return to its predisturbance configuration. If it exceeds that threshold, it may move to a new configuration. Furthermore, most ecological systems change not only in response to disturbance but also in response to natural, successional change. Constancy and resiliency have this in common: both focus on species persistence and abundance as measures of stability.

• Dynamic stability - A system is dynamically stable if its future states are determined largely by its own current state, with little or no reference to outside influences. In many ways, this seems to correspond with our intuitive notions of stability and to make sense of the relationship between diversity and stability. The more complex the ecosystem, the more successfully it can resist a stress. A system that is dynamically stable is one that is relatively immune to disturbance. It reflects our hope that stable systems should be able to maintain themselves without human intervention.

Proposal of Ecosystem Properties

ResilienceEcosystem resilience. Ecosystems are communities of plants, animals, fungi and micro organisms that interact with each other and their physical environments. The ability of an environment to tolerate pressures and maintain its life-supporting capacity can be called ecosystem resilience. Ecosystem resilience is a complex concept, we do have a range of environmental indicators to report whether ecosystems and environments are coping with pressures or are degrading over time. A common description of resilient systems is that such a system must have the following three abilities: • The ability to respond, quickly and efficiently, to regular disturbances and threats. • The ability continuously to monitor for irregular disturbances and threats, and to revise the basis for the monitoring when needed. • The ability to anticipate future changes in the environment that may affect the system’s ability to function, and the willingness to prepare against these changes even if the outcome is uncertain. Resilience engineering provides the methods by which a system’s resilience can be gauged or measured, and the means by which a system’s resilience can be improved. Resilience has previously proven to be a useful construct in analyzing the persistence, stability and flexibility of ecological systems, for many decades.

Proposal of Ecosystem Properties

Idea - The game of CHESS

1. It have rules as the negotiation process has.2. A computational approach generates a tree of all decisions and counterdecisions.3. The strategy is the heuristics to search decisions (movements) throughout the tree.

Meta Recommender STYLES

The Slav formation

The Scheveningen formation

The Slav formationOpenings: Primary: Slav. Other: Catalan, Grunfeld, Colle system Character: Slow-paced game.

The Sicilian - ScheveningenOpenings: Primary: Sicilian (Najdorf, Richter-Rauzer and Sozin variations), Sicilian Scheveningen, Character: Complex, dynamic, sharp middlegame.

Individual Partner 1 Individual Partner 2

Individual Partner 2Individual Partner 1

Conflict of Interest

Tolerance Avoidance

Aggression

Reconciliation

If Conflux of Interest

Meta Recommender STYLESRelation and negotiation styles

Assumption

In a negotiation (relational) model, aggressive behaviour is one way of settling conflicts of interest. Other way s are tolerance (sharing of resources) or avoidance of confrontation

If strong mutual interest in maintenance of the relationship exists, parties negotiate the terms of their relationship by going through cycles of conflict and reconciliation

Meta Recommender STYLES

• Dominance– Despotic – Tolerant– Egalitarian

• Cooperation– Grooming Compromise– Testing of Dominance– Accepted Dominance– Tit-for-tat Transactions

Relation styles

Meta Recommender STYLES

Dominance style refers to the way that dominants treat subordinates and vice versa, in competitive contexts:

Despotic. Dominants exploit subordinates with impunity, and punish them for transgressions,

Tolerant. Dominants and subordinates have close relationships. High level of tolerance around contested resources,

Egalitarian. As well as tolerant, but also subordinates are capable of mounting effective alliances to curb the power of dominants.

4. Relation styles (DOMINANCE)

Meta Recommender STYLES

In Cooperation style, the tension between competition and cooperation works itself out in a negotiation process characterized by:

Grooming Compromise. First encounter, a combination of conflict and conflux of interests, and a cycling through positive and negative interactions resulting in some sort of agreement about the ‘proper’ terms of the relationship.

Testing of Dominance. A mechanism of conditional reassurance, partners test one another and collect information about how each one of them assess the relationship.

Accepted Dominance. Social arrangement with room for sharing, tolerance and alliances from below. Dominance depends on acceptance from below.

Tit-for-Tat Transactions. A transaction is defined as a cooperative relationship that serves the interests of both parties, continuation of which is contingent upon continued pay-offs.

4. Relation styles (COOPERATION)

Meta Recommender STYLES

AggressiveCooperative

Passive

Active

Flexible / Aggressive

(Competitive)

Flexible / Cooperative

(Collaborative)

Analytic / Cooperative

(Accommodating)

Analytic / Aggressive

(Avoiding)

[Blake and Mouton, 1969]

4. Negotiation styles

Meta Recommender STYLES

[Kozicki, 2005]

Avoiding style: as a stable trait on how to come to an agreement, which is characterised bv values and beliefs reflecting an unassertive and uncooperative orientation? This style is often used when the person does not want to engage in a meaningful negotiation. It is often seen in an environment when a junior person is negotiating with a senior manager;

Accommodating style: a predisposition towards an accommodating negotiation style is defined as a stable trait on how to reach an agreement, which is characterised by values and beliefs reflecting an unassertive and cooperative orientation. This style is used when the negotiator puts a considerable focus onto relationship at the expense of the deal;

4. Negotiation styles (PASSIVE)

Meta Recommender STYLES

[Kozicki, 2005]

Competitive style: a predisposition towards a competitive negotiation style as a stable trait on how to come to an agreement, which is characterised by values and beliefs reflecting an assertive and uncooperative orientation. This style is normally used by the 'winner takes all' approach. Focus is on the deal at the expense of the relationship;

Collaborative style: a predisposition towards a collaborative negotiation style as a stable trait on how to reach an agreement, which is characterised by values and beliefs reflecting an assertive and cooperative orientation. This style is often used by people who adopt a more problem-solving approach to the negotiation. Their style is more around the pie can be expanded as compared to a scarcity approach.

4. Negotiation styles (ACTIVE)

Meta Recommender STYLES

• Dominance– Despotic – Tolerant– Egalitarian

• Cooperation– Grooming Compromise– Testing of Dominance– Accepted Dominance– Tit-for-tat Transactions

• Avoiding• Accommodating• Competitive• Collaborative

4. Correspondence of Relation and Negotiation styles

Meta Recommender STYLES

Structural conditions of interactions

3 hypothesis:

H1. The higher diversity of interactions, the higher potential for innovations.H2. The more versatile roles an agent assumes, the greater his potential for innovationsH3. The higher the structural imbalance (forbidden closed relationships), the greater the potential for innovations.

Example Scenarios

Scenario 2Scenario 2

Monitor Measurement:Medium Diversity/Interaction (H>1.5)Local (H - I)Complexity variable (1.2<C<2.3)

Screening:Relational Model Cooperation/Testing of Dominance

Meta Recommendation test one another and collect information about how to assess the relationshipStyle: collaborative

Scenario 3Scenario 3

Monitor Measurement:High Diversity/Interaction (H>2.8)Global (H –I)Complexity stable (C≈2.5)

Screening:Reputation Model Dominance/Egalitarian

Meta Recommendation mount effective alliances to curb the power of dominantsStyle: accommodating

Interaction densitiesLowMediumHigh

Scenario 1Scenario 1

Monitor Measurement:Low Diversity/Interaction (H<1.2)Global (H – I)Complexity increase (C>2)

Screening:Relational Model Cooperation/Grooming Compromise

Meta Recommendation agreement about the ‘proper’ terms of the relationshipStyle: competitive

Example Scenarios

Eco. Monitor and Meta Recommender Algorithm

Step 1. Call ecosystem properties measurement Step 2. Compare reference properties and measures

If Lower/Medium/Higher Diversity/Interaction (H), andGlobal/Local (H – I), andComplexity increase/decrease/stabilize (C)Then get agent resources components and compare them

Step 3. Call Relation and Negotiation Styles ScreeningStep 4. Meta Recommendation (change relation and negotiation styles to meet the reference properties)Step 5. Goto Step 1

Example Scenarios

Two mechanisms• Polling – anonym information that is polled from participating agents / SME• Public information

What data?• Negotiation models P2P repository• Negotiation instances and their success compared to the expected success• • • •

Public data to feed the monitor

THANK YOU

2nd Tech. Meeting, Girona 2-3-4/04/07

Contract no. 03744

Ecosystem Approach Introduction

Negotiation as a football match

• Establish Social StructureBetter EfficiencyTask Partition

– Maintain Order– Reduce Conflicts– Promote Co-operation

• Social Structure– Results of Conflicts– Conditioned by:

• Physical Features• Previous Results of Contests

Ecosystem Approach Hierarchical Societies

• Definition of a Model to study Computational Ecologies– Competition for Bounded Resources– Selection on Expectations– Incomplete Information (Delayed)

• Results– Heterogeneity – Reward Mechanism System Stabilisation

Chaos In Distributed Systems

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Simple Model rr

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rfFraction of Agents Using r

IndecisionBad Performance

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Probability for new agents to be of type s

typesf

Fraction of Agents of Type s in the System

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Fraction of Agents of Type s Using r

Model with Heterogeneity rss

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Utility for agent s when using r

Our Approach [de la Rosa & Muñoz 2000]

Opponent Difficulty

Heterogeneity

Heterogeneity Metric