3. FENOMEN MUSICAL
3.1. LA MÚSICA COM A VEHICLE D’EVANGELITZACIÓ
3.1.2. CONTROL SOCIAL
Innovation in technology generally refers to inventions that include ‘‘exploitation’’ and ‘‘exploration’’, both of which are also fundamental processes of other adaptive systems [395]. For innovation, exploration refers to activities that involve the creation of new knowledge, technologies and products. Conversely, exploitation generally refines and improves existing knowledge, technologies, and products. Prior studies agree that the precise definitions of exploitation and exploration are
Fig. 18. (A) Comparison of the efficiency of different research strategies in discovering the knowledge network in which nodes are chemical and two
chemicals are links if they appear in the same article or patent abstract. Efficiency is defined as the estimated number of experiments required to discover from 1% to 100% of the network. The strategies include random choice, the inferred MEDLINE strategy, and optimal strategies for discovering 20%, 50%, and 100% of the network. (BCD) illustrate the realizations of the real strategy and the optimal strategies in the knowledge network.
Source: The figure is reprinted from Ref. [392].
still lacking because they both attach importance to learning, improvement, and the acquisition of new knowledge. The primary ambiguity is whether these two activities are differentiated by the type of learning or the existence of learning. Gupta et al. argued that it is more logical to differentiate between exploration and exploitation by using the former criteria [396]. The trade-off between exploration and exploitation is essential for solving numerous problems. Specifically, a choice must be made between searching for new solutions and improving existing solutions [395–400]. Because exploration is a riskier process, it generally leads to more uncertain and distant benefits than exploitation. A proper balance between exploration and exploitation is considered to be critical for adaptive behavior in humans and other animals [395,400,401].
Combination may be a primary method for innovation during the process of exploration and exploitation. Youn et al. analyzed US patent records from 1790 to 2010 and characterize invention as a combinatorial process because it identifies distinct technologies and their combinations with patent technology codes [402]. In the context of combination, because time evolves, the larger the pool of inventions that are accumulated, the higher the probability of generating new inventions by using combination processes, which ultimately leads to a singularity that the innovation system transitions to super explosive growth and the total number inventions diverges. To better understand the evolutionary dynamics of innovation combination and the condition for singularity, Sood [403] modeled combinational innovation dynamics as an interacting branching process where each mating pair of new inventions and old inventions produces a certain number of new inventions that follow a Poisson distribution with mean p, and old inventions expire with a characteristic probability p
/τ
. Using theoretical and numerical analysis, they reported that no phase transition occurs and when p≪
1 andτ = ∞
, the surviving processes that occur prior to a super explosive phase follow a long period of a quiescent state. Soléet al. [404] extended the pairwise combination model to a multiple combination case and consider different choices of aging functions, e.g., power-law aging and exponential aging. Based on mean-field theory analysis, they found that singularity emerges when the long-range memory mechanism is used; however, if the aging has a characteristic time scale, instead of singularity, a black hole of old invention presents and slows down the rate of invention creation.In certain situations, we cannot derive an analytic expression for innovation dynamics; agent-based modeling (ABM) [405] is an alternative for analyzing the basic rules that govern the innovation system and their relationships among these rules and for proposing effective guides and strategies to optimize the system. Berger-Tal [406] developed an agent-based model with time varying exploration strategy to pursue certain goals (e.g., energy, money or prestige) to investigate the trade- off between exploration and exploitation. In their model, four distinct phases are produced: knowledge establishment, knowledge accumulation, knowledge maintenance, and knowledge exploitation. During a subject’s life-span, the four knowledge phases are mutually transitioned and occur multiple times according to the changing environment, which implies that the optimal solution to the exploration and exploitation trade-off depends on the subject’s life-stage and current environmental conditions.
For firms, the Research and Development (R&D) process is crucial for preserving their competitiveness and increasing market share. Forming R&D alliances is one important development strategy where the engaged firm can gain access to different assets more quickly, enlarge their technology pool more effectively and incur fewer costs and less risk than they could individually [407]. The allied R&D process is often modeled as agents moving in a knowledge space to search for potential technology and exchanging acquired knowledge among alliances. Using ABM, Tomasello et al. [408] studied the exploration process with partner selection and rewiring. They found that firms tend to form clusters with the number of clusters depending on the rewiring rate and interaction radius. The exploration performance was defined as the total move distance. It has an inverted U-shaped dependence on the two parameters.
7.2.2. Innovation networks
Communication networks. The trade-off between exploitation and exploration is particularly important when individ-
in the population, while the exploitation process diffuses good solutions to increase the overall performance of the group. The interacting social networks of individuals will highly affect how the information of novel solutions is disseminated in global groups. This situation is often modeled as a group of individuals searching for optimal solutions regarding rugged landscapes [414–418]. The primary difficulty of this type of search process is the presence of local optima. As a result, groups seek to reach the optimal solution by avoiding local optima with various communication structures.
Generally, the effect of communication structures on innovation creation and propagation continues to be debated. In certain early studies [409,414,419], they found that when facing complex problems, networks of agents that exhibit lower efficiency can outperform more efficient networks, where ‘‘efficiency’’ refers to the speed that information regarding trial solutions can spread throughout the network. For example, Lazer and Friedman [414] conducted an agent-based simulation and demonstrated that a ‘‘locally connected lattice’’ outperforms a ‘‘fully connected network’’ for determining the optimal solutions in the long run. Mason and Watts [415] conducted a series of 256 online behavioral experiments where groups of individuals solve complex tasks with eight different interacting networks. They reported a conflicting result: efficient networks (that promote faster information flow in the population) outperform inefficient networks as related to the average success of group members. In addition, network efficiency affects the distribution of individual success. Derex and Boyd [416] modeled the innovation problem in a more realistic manner. Each individual is provided with certain ingredients to create a new ingredient; the created ingredient can then be used to create a high-level ingredient. Different combinations of ingredients lead to the failure or success of ingredients with different scores. Their experiment demonstrates that when individuals learn from other successful individuals, fully connected groups strongly reduce cultural diversity, while partially connected groups can provide more diverse solutions. This diversity is critical because it allows groups to develop complex solutions that are impossible for fully connected groups to develop.
In addition to the effect of special communication structures on collective performance for social problems, the influence of social learning strategies that are used by individuals and organizations are valuable because disregarding these strategies might not produce the desired effects of a given structure. The agent-based simulation that was conducted by Barkoczi and Galesic [420] revealed that the social learning strategies of individual members significantly affect the effectiveness of certain communication networks on group performance. Specifically, by applying two social learning strategies (i.e., best member and conformity) and eight network structures that include a broad range of possible topologies, they found that inefficient network structures outperform efficient network structures when individuals adopt the best member strategy, and the opposite occurs when the conformity strategy is adopted. In addition, they found that groups that rely on the best member strategy perform well for simple tasks, while complex tasks need groups that follow the conformity strategy based on a small sample of other individuals.
Patent citation networks. Compared to scientific knowledge innovation, technology innovation is more clearly defined.
Each citation for technology is interest related; therefore, the redundant citing links are much less significant than for the scientific domain. In addition, the patent office plays a key role in issuing new patents and the patent codes fully encapsulate the novelty that is clearly delineated in the claims. In a patent citation network where each patent serves as the vertex, the presented citation between two patents, e.g., patent A and B, indicates that patent A is partly built on patent B. Therefore, the entire patent citation network may resemble a network of idea production, combination and propagation and subsequently can be used to map the technological trajectories that record incremental innovation and breakthroughs.
Verspagen [421] extended the method used in Ref. [422] and analyzed the scientific citation network of publications on the discovery of DNA. He discovered the primary flows of ideas by using the extracted backbone of the patent citation network. The application of this method to fuel cells reveals the historical dynamics of fuel cell research beginning with broad exploration in various directions followed by a focus on persistent and evolutionary interpretable results for further exploitation. This selective and persistent nature is shared by other technological trajectories. Acemoglu, Akcigit and Kerr [423] constructed the technology innovation network from 1.8 million US patents and their citation properties. In this network, each node represents a technology field, and the citation links are weighted by relative citations. A regression model is proposed to investigate the effect of network structures and patent growth in upstream technology fields on a certain technology field’s future development. It is found that innovation advances in one section of the network significantly impact nearby technology fields. If a technology class includes more prior upstream innovations, it tends to have more innovations in the future. However, this effect is limited to local areas within the network. To clarify, innovation of technologies will not accelerate the innovation of distant technologies in the network.
Software networks. Software systems are one of the most influential systems in modern society and recently have been
utilized to study the evolution of technology. A software system includes numerous units that interact with each other when designing, coding and executing software. Although software engineering is purpose-driven, it shares numerous similar characteristics with patents. One of most important common characteristics is the use of a combination of existing codes and new codes to achieve new functional requirements of software systems. The benefit of combination is a reduction in the duplication of effort and acceleration in the progress of development. Because rich empirical data are available, it is possible to understand the innovation processes of software systems.
A software system can be modeled using complex networks where nodes represent classes, motifs, patterns, libraries, packages, subsystems and components, and edges represent dependency relationship between the nodes. Complex networks have been used to analyze software systems at different scales that range from software motifs [424] to collaboration graphs [425]. Empirical studies regarding the networks that are extracted from software systems reveal several key structural properties. Generally, these networks follow power-law degree distributions [426–428], exhibiting small-world
properties [426], community structures [426,429] and various other statistical topological features [430–432]. The evo- lution of software networks has attracted widespread attention. Most software systems obey certain structural evolution laws [425,430,433–435]. In addition, certain models have been proposed for software network evolution and describe the evolutionary mechanisms from different perspectives such as refactoring processes [426], software patterns [436], modular attachment [437] and multi-level networks [438].
Distributed development makes the design of large-scale programs possible. However, reusing existing code may result in the emergence of incompatibilities among software packages, which leads to failures in the functionality of certain packages and makes the system unstable [433]. The perspective of complex systems encourages researchers to analyze systems from a local and global aspect, which helps to identify key nodes in the system that ensure the code combination, encapsulation and maintenance and enhance the system’s robustness.