too fragmented and no more learning by doing can occur, or it forms a system of firm-rule hypercycles. With no processes to introduce new firms or rules to the system, dead systems cannot be resurrected and live systems are unable to change much except by a particularly unlucky sequence of rule decay events. (] however contains proposals for future extensions to the basic hypercycles model that will make it more open-ended.) 1. Summary of model comparison & Unit of Unit Value: Sources of Emergent key knowledge pre-defined novelty patterns or or technology endogenous L&F Workers’ Bit strings NK fitness Convergence [] beliefs / landscape on peak / routines Mutation optimum S&V Nodes in a Height & Chance Scale-free [, ] grid connection discovery distribution of network to baseline from advances of nearby connected nodes nodes A&P Systems of List of Scale-free [] NAND desired logic distribution of logic gates functions obsolescence sizes SKIN Ordered Endogenous Social [, , ] triples of demand for of networks; “kenes”: integers inputs kenes; new Scale-free abilities, abilities distribution of expertise production network sizes CJZ Firms’ Vector of Endogenous Not Social [] knowledge continuous demand for specified networks variables collaboration partners Firms’ Role in Emergence production hypercycles of [, , ] skills chemistry system system of firms (rules) system & rules Nearly all the models assume pre-defined structures. L&F assume a fitness landscape, for which they use Kauffman’s NK fitness. S&V assume a network structure for their percolation grid technology space. A&P assume their list of desired functions. The hypercycles model assumes the initial structure for the network of firms, the initial allocation of rules to firms, and the various types of rule themselves (the For this latter assumption Padgett’s studies so far have restricted “chemistry”).
too fragmented and no more learning by doing can occur, or it forms a system of firm-rule hypercycles. With no processes to introduce new firms or rules to the system, dead systems cannot be resurrected and live systems are unable to change much except by a particularly unlucky sequence of rule decay events. (] however contains proposals for future extensions to the basic hypercycles model that will make it more open-ended.) 1. Summary of model comparison & key Unit of knowledge or technology Unit Value: pre-defined or endogenous Sources of novelty Emergent patterns L&F [] Workers’ beliefs / routines Bit strings NK fitness landscape Mutation Convergence on peak / optimum S&V [, ] Nodes in a grid network Height & connection to baseline Chance discovery from nearby nodes Scale-free distribution of advances of connected nodes A&P [] Systems of NAND logic gates List of desired logic functions Scale-free distribution of obsolescence sizes SKIN [, , ] “kenes”: abilities, expertise Ordered triples of integers Endogenous demand for inputs of kenes; new abilities Social networks; Scale-free distribution of production network sizes CJZ [] Firms’ knowledge Vector of continuous variables Endogenous demand for collaboration partners Not specified Social networks [, , ] Firms’ production skills (rules) chemistry Role in hypercycles system Emergence of system system of firms & rules Nearly all the models assume pre-defined structures. L&F assume a fitness landscape, for which they use Kauffman’s NK fitness. S&V assume a network structure for their percolation grid technology space. A&P assume their list of desired functions. The hypercycles model assumes the initial structure for the network of firms, the initial allocation of rules to firms, and the various types of rule themselves (the “chemistry”). For this latter assumption Padgett’s studies so far have restricted
Simulating%20innovation_Paper_ESSA2013_Rev.docx