Agents, artifacts and innovation David Lane, Univ Modena and Reggio Emilia and Santa Fe Institute Roberto Serra and Marco Villani, UMRE ISCOM project (FET-IST)

Premise: a theory of artifact innovation All artifacts have a history -- as do the modes of interaction among people in which artifacts figure All artifacts have a history -- as do the modes of interaction among people in which artifacts figure Propose to develop a theory about the processes through which artifact histories are realized Propose to develop a theory about the processes through which artifact histories are realized How new artifacts come into being How new artifacts come into being How their tokens proliferate and become incorporated into patterns of human interaction How their tokens proliferate and become incorporated into patterns of human interaction How new patterns of interaction among human beings and the artifacts they create are generated How new patterns of interaction among human beings and the artifacts they create are generated

Primary questions What kinds of social structures support the processes of artifact innovation? What kinds of social structures support the processes of artifact innovation? How do these structures modulate the conflicting functionalities that underlie proliferation of artifact tokens and generation of new artifact types? How do these structures modulate the conflicting functionalities that underlie proliferation of artifact tokens and generation of new artifact types? How do these structures transform themselves as they incorporate new kinds of functionality around new kinds of artifacts? How do these structures transform themselves as they incorporate new kinds of functionality around new kinds of artifacts? Positive feedback dynamic, linking proliferation of new artifacts, new patterns of human activity that organize around these artifacts, generation of new types of artifacts… Positive feedback dynamic, linking proliferation of new artifacts, new patterns of human activity that organize around these artifacts, generation of new types of artifacts…

Theory as ontology “Theory of innovation” an oxymoron? “Theory of innovation” an oxymoron? Aim to present an ontology for phenomena associated with artifact innovation Aim to present an ontology for phenomena associated with artifact innovation Kinds of entities Kinds of entities Interaction modalities Interaction modalities How entity properties change as a result of interactions How entity properties change as a result of interactions Value of theory established and demonstrated through dialogues Value of theory established and demonstrated through dialogues With historical narratives books Lonworks With historical narratives books Lonworks With mathematical models With mathematical models

Reciprocality principle Agents: human beings, or organizations “in the name of which” human beings act Agents: human beings, or organizations “in the name of which” human beings act Artifacts: entities constructed by human beings, for the use of human beings Artifacts: entities constructed by human beings, for the use of human beings Reciprocality principle: Reciprocality principle: the generation of new artifact types is mediated by the transformation of relationships among agents; and the generation of new artifact types mediates the transformation of relationships among agents.

Agent-artifact space Horizontal and vertical relations Horizontal and vertical relations Network ties Network ties Agent-agent: recurring patterns of interaction, directed towards transformations of artifacts, artifact relations, or agent-artifact relations Agent-agent: recurring patterns of interaction, directed towards transformations of artifacts, artifact relations, or agent-artifact relations Agent-artifacts: relations of production, ownership, use Agent-artifacts: relations of production, ownership, use Artifact-artifact: functional substitutibility, complementarity (co- use) Artifact-artifact: functional substitutibility, complementarity (co- use) Recursive structures Recursive structures

Agent properties Resources artifacts Resources artifacts Permissions potential field of relationships Permissions potential field of relationships Cognitive structures shared, distributed Cognitive structures shared, distributed Attributions and directedness Attributions and directedness Narratives Narratives

Two kinds of innovation Better-faster-cheaper Better-faster-cheaper New attributions of functionality New attributions of functionality

Organization of agent-artifact space Locus of new attributions: generative relationships Locus of new attributions: generative relationships Market systems Market systems Competence networks Competence networks Scaffolding structures Scaffolding structures

Positive feedback dynamics Exaptive bootstrapping Exaptive bootstrapping

Model and theory The theory provides The theory provides an ontology that determines the model entities and processes an ontology that determines the model entities and processes a language to describe the different “histories” a language to describe the different “histories” some specific assumptions and consequences that can be tested, in a mathematical model or by confrontation with data some specific assumptions and consequences that can be tested, in a mathematical model or by confrontation with data The model to be described here allows a precise statement of the theory, in a limited context The model to be described here allows a precise statement of the theory, in a limited context Developing the model and analyzing its results can give rise to a process leading to modifications of the theory itself Developing the model and analyzing its results can give rise to a process leading to modifications of the theory itself

Constraints from the theory Reciprocality Reciprocality => Agents, artifacts and relations among them must be represented Innovation leads to modifications of the of agents as well as of the of artifacts Innovation leads to modifications of the role of agents as well as of the meaning of artifacts => both must be endogeneously generated External fitness functions make no sense Directedness (towards transformations of artifact space) Directedness (towards transformations of artifact space) => agents have intentionality

Model: homo faber agents use artifacts, produced by other agents, to build artifacts, which can be used by yet other agents agents use artifacts, produced by other agents, to build artifacts, which can be used by yet other agents the meaning of artifacts is defined by which agents use them, for what the meaning of artifacts is defined by which agents use them, for what the role of agents is defined by what they make, and by the agents with which they interact the role of agents is defined by what they make, and by the agents with which they interact gift economy gift economy

Artifacts Artifacts are (currently) represented by numbers Artifacts are (currently) represented by numbers Agents produce (numbers) by applying functions (to numbers) Agents produce (numbers) by applying functions (to numbers)

Agent properties production recipes production recipes ( ) produces 5 ( ) produces 5 goals goals i.e. (roughly) what new artifacts it wants to produce (more precisely: from which existing artifacts it wants to exapt) i.e. (roughly) what new artifacts it wants to produce (more precisely: from which existing artifacts it wants to exapt) stocks stocks list of artifacts known to the agent list of artifacts known to the agent list of other agents known to the agent list of other agents known to the agent score that it attributes to its relationships with other agents score that it attributes to its relationships with other agents “style” “style” A set of parameters which determines the propensity of the agent to innovate, etc.

Standard dynamics, without innovation at each time step an agent at random is selected for updating at each time step an agent at random is selected for updating for each recipe, it searches (among the stocks of its suppliers) for the required inputs for each recipe, it searches (among the stocks of its suppliers) for the required inputs if the inputs are found, the product is produced and inserted immediately into the stock if the inputs are found, the product is produced and inserted immediately into the stock the stocks of the suppliers are reduced the stocks of the suppliers are reduced If one of the inputs is not found, the agent searches for another artifact of the same type from its acquaintances If found, same as before Otherwise, the agent passes to the following recipe and the counter associated to the unused recipe is updated

External market A B C 1 D E 2 F G 3 H I J 4 K 5 L M 6 N O Raw material Initial conditions: external market

A B C 1 D E 2 F G 3 H 4 K 5 L Raw material

Innovation an agent can modify its products (by creating a new recipe), OR an agent can modify its products (by creating a new recipe), OR it can enhance its knowledge of other agents it can enhance its knowledge of other agents

Creating new recipes: goals the agent the agent looks at the portion of artifact space which it knows looks at the portion of artifact space which it knows chooses a specific goal to pursue, based upon an artifact which it does not produce chooses a specific goal to pursue, based upon an artifact which it does not produce tries to “come close” to that goal by using the available operators on the available inputs tries to “come close” to that goal by using the available operators on the available inputs if the agent succeeds (within a given range), it puts the new recipe in its set of active recipes if the agent succeeds (within a given range), it puts the new recipe in its set of active recipes

Goal setting by imitation A B C D EF G H N M L J P O I K Imitation (of a randomly selected artifact) is a simple way to sample artifact space the clusters of artifacts are likely to be high reward zones The agent tries to build an artifact similar (within a given threshold) to the selected one (which represents the “goal” of the imitation) Q

Generating a new recipe new recipes can be generated by new recipes can be generated by Changing the order of the inputs (leaving the support unaltered) Changing the order of the inputs (leaving the support unaltered) changing some inputs changing some inputs Changing the order of the operators Changing the order of the operators Changing the operators (eg by crossing different recipes) Changing the operators (eg by crossing different recipes)

Imitation world Imitation can produce self-sustaining loops, and the system is able to survive Imitation can produce self-sustaining loops, and the system is able to survive Introduction of successful novelties ends after a transient period of time Introduction of successful novelties ends after a transient period of time

Goal setting by “jumping” A B C R Q O F D I E M L G N P S H The jump allows the exploration of new regions of artifact space

Social innovation In general, an agent has a limited knowledge of artifacts and other agents In general, an agent has a limited knowledge of artifacts and other agents An agent can also try to innovate together with another agent, chosen from among those it knows An agent can also try to innovate together with another agent, chosen from among those it knows Agents that cooperate share recipes and cross them to create new ones Agents that cooperate share recipes and cross them to create new ones Agents can acquire new ties, with whom they do not have a customer-supplier relationship Agents can acquire new ties, with whom they do not have a customer-supplier relationship

Question 1: Intentionality What difference do goals make? This can be investigated by comparing a goal- directed system, with a system without goals This can be investigated by comparing a goal- directed system, with a system without goals The non-goal-oriented system (NG) is generated by allowing an agent to develop a new recipe by combining some of its inputs and operators chosen at random The non-goal-oriented system (NG) is generated by allowing an agent to develop a new recipe by combining some of its inputs and operators chosen at random

Diameter of artifact space LEFT: WITH GOALS RIGHT: WITHOUT GOALS

Current artifacts Left: with goals Right: NG

Average number of recipes per agent, fraction of known agents

What difference do goals make? Preliminary answers No-Goal world is more robust (and predictable) No-Goal world is more robust (and predictable) Goals generate much broader and diversified artifact space, with greater diversity in agent fates Goals generate much broader and diversified artifact space, with greater diversity in agent fates

Question 2: Innovative activity Question 2: Innovative activity What is the relationship between agents’ propensity to innovate and structure of artifact space? We can vary parameters that control agents’ innovation rate, jump frequency, jump size We can vary parameters that control agents’ innovation rate, jump frequency, jump size We can compare systems in which agents all have the same innovation propensities with systems in which they are heterogeneous We can compare systems in which agents all have the same innovation propensities with systems in which they are heterogeneous

Homogeneous agents: left: diameter (per agent) right: number of artifacts currently in system

Innovators prevail in heterogeneous system: left: dist of innov prob; right: no. of successful projects as function of innov prob

Varying jump frequency and range High jump probability in homogeneous systems increases the diameter of artifact space, but makes the system more fragile (a higher proportion of agents die) -- the more agents, the more fragile High jump probability in homogeneous systems increases the diameter of artifact space, but makes the system more fragile (a higher proportion of agents die) -- the more agents, the more fragile In heterogeneous systems, the frequent jumpers develop fewer successful projects In heterogeneous systems, the frequent jumpers develop fewer successful projects BUT if jump range also varies, a mixture of short and long jumpers performs very well -- and the latter outperform the former! BUT if jump range also varies, a mixture of short and long jumpers performs very well -- and the latter outperform the former!

Question 3: agent relationships What produces stable relationships? Are these generative? We compare system effects when agents choose innovating partners randomly with those when agents use a criterion to choose partners We compare system effects when agents choose innovating partners randomly with those when agents use a criterion to choose partners Criteria examined include: past success; aligned directedness; role heterogeneity Criteria examined include: past success; aligned directedness; role heterogeneity

Scoring relationships on the basis of past success

Choosing partners according to V This criteria gives rise to reciprocal relationships that are very stable in time (though rarely last “forever”) This criteria gives rise to reciprocal relationships that are very stable in time (though rarely last “forever”) Partnering on the basis of V gives rise to substantially richer artifact spaces than does random pairing Partnering on the basis of V gives rise to substantially richer artifact spaces than does random pairing

Formation of successful partnerships Left: partnering based on V Right: random partnering

Effect on artifact space

Generative potential Aligned directedness Aligned directedness closeness of goals Heterogeneity Heterogeneity difference between variance of outputs -- specialists pair with generalists Mutual directedness Mutual directedness satisfying history -- V Permissions Permissions Action opportunities Action opportunities

Partnering on generative potential All three measured criterion alone produce results similar to those for V All three measured criterion alone produce results similar to those for V Some combinations tend to do even better than the single criteria… Some combinations tend to do even better than the single criteria…