Modeling Cultural Dynamics

genetic algorithm is a minimal computer model of natural selection that made it possible to investigate the effect of manipulating specific parameters on the evolutionary process. If culture is, like biology, a form of evolution, it should be possible to similarly abstract the underlying skeleton of the process and develop a minimal model of it. Meme and Variations, or MAV, is a computational model, inspired by the genetic algorithm, of how ideas evolve in a society of interacting individuals (Gabora 1995). The name is a pun on the classical music form 'theme and variations', because it is based on the premise that novel ideas are variations of old ones; they result from tweaking or combining existing ideas in new ways (Holland et al. 1981). MAV explores the impact of several phenomena that are unique to culture. These are introduced briefly here, and the technical details of how they are implemented will be presented shortly.

2003

Although cultural evolution clearly outpaces genetic evolution in the natural world due to its higher rates of reproduction, recombination and selection, it does so built on biological foundations. In the natural world, cultural change takes place in minutes, days, years or decades, whereas genetic change takes at least a decade and a half. In the natural and cultural worlds the media of evolutionary transmission behave differently: genes reproduce slowly; ideas reproduce quickly. In the artificial world of the computer, whether modeled on a cultural or genetic metaphor, the medium in which evolution unfolds is the same for both, and the generations through which they both unfold is regulated by same the system clock. Consequently, there is no a priori reason to assume that cultural processes will be quicker than genetic ones in an artificial world, simply because they are quicker in the natural world. Cultural algorithms may be faster, but if they are it is for more complex reasons, such as their richer combinatorial possibilities (ideas may come from anywhere, zygotes only come from couples having sex), their greater range of generational longevity (from fleeting notions to commandments carved in stone), and the varieties of their modes and units of selection. It seems likely that a science of culture may enrich evolutionary computation by offering a superset of evolutionary mechanisms to explore. Evolutionary computation will surely enrich a science of culture by offering a superset of modeling practices. Such a coevolutionary synthesis may be fruitful to explore. Natural and Artificial Culture Empirically, culture is the product of individuals, artifacts, and their interactions at varying levels of complexity. Variation is omnipresent and requires explanation. Cultures are different. Its members are different. Its members' heads are filled with different thoughts. Moreover, cognition is distributed among people and technology. Culture emerges from these objects (thoughts, people, artifacts) through multiagent webs of mutual causation. Cultural processes are parallel and simultaneous. These complexities remain largely intractable to discursive and mathematical representations. The "new sciences of com

2011

How did human creativity arise? An agent-based model of the origin of cumulative open-ended cultural evolution.

SSRN Electronic Journal, 2000

We present an individual based model of cultural evolution, where interacting agents are coded by binary strings standing for strategies for action, blueprints for products or attitudes and beliefs. The model is patterned on an established model of biological evolution, the Tangled Nature Model (TNM), where a 'tangle' of interactions between agents determines their reproductive success. In addition, our agents also have the ability to copy part of each other's strategy, a feature inspired by the Axelrod model of cultural diversity. Unlike the latter, but similarly to the TNM, the model dynamics goes through a series of metastable stages of increasing length, each characterized by mutually enforcing cultural patterns. These patterns are abruptly replaced by other patterns characteristic of the next metastable period. We analyze the time dependence of the population and diversity in the system, show how different cultures are formed and merge, and how their survival probability lacks, in the model, a finite average lifetime. Finally, we use historical data on the number of car manufacturers after the introduction of the automobile to the market, to argue that our model can qualitatively reproduce the flurry of cultural activity which follows a disruptive innovation.

The speed and transformative power of human cultural evolution is evident from the change it has wrought on our planet. This chapter proposes a human computation program aimed at (1) distinguishing algorithmic from non-algorithmic components of cultural evolution, (2) computationally modeling the algorithmic components, and amassing human solutions to the non-algorithmic (generally, creative) components, and (3) combining them to develop human-machine hybrids with previously unforeseen computational power that can be used to solve real problems. Drawing on recent insights into the origins of evolutionary processes from biology and complexity theory, human minds are modeled as self-organizing, interacting, autopoietic networks that evolve through a Lamarckian (non-Darwinian) process of communal exchange. Existing computational models as well as directions for future research are discussed.

2000

The Construct technical report describes the Construct model and lists the theories which it incorporates. Scientific literature that has used the model is listed as well as representative examples of real-world use within organizations. The report also defines the input and output variables and describes the various input and output files used with Construct. System requirements and performance characteristics are

Systems Research and Behavioral Science, 2013

Agent-based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to build a theoretical agent-based model to simulate the social evolution of a set of agents/artificial societies. In this model, each agent (artificial society) has an available number of social behaviours that compete among each other. The agent/agent interactions are carried out by their social otherwise, the agent/environment interactions are expressed through the consumption of ecological resources in repression and satisfaction by social behaviours of the agent. In this work, we will present the structure and the formulation of our model. This model can capture emergent social phenomena such as globalization and polarization, and it can help to understand the social migration process.

Frontiers in Neurorobotics, 2023

International Journal on Software Tools For Technology Transfer, 2013

There are both benefits and drawbacks to cultural diversity. It can lead to friction and exacerbate differences. However, as with biological diversity, cultural diversity is valuable in times of upheaval; if a previously effective solution no longer works, it is good to have alternatives available. What factors give rise to cultural diversity? This paper describes a preliminary investigation of this question using a computational model of cultural evolution. The model is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. Numerical simulations indicate that the diversity of ideas in a population is positively correlated with both the proportion of creators to imitators in the population, and the rate at which creators create. This is the case for both minimum and peak diversity of actions over the duration of a run.

2016

We present an individual based model of cultural evolution, where interacting agents are coded by binary strings standing for strategies for action, blueprints for products or attitudes and beliefs. The model is patterned on an established model of biological evolution, the Tangled Nature Model (TNM), where a ''tangle'' of interactions between agents determines their reproductive success. In addition, our agents also have the ability to copy part of each other's strategy, a feature inspired by the Axelrod model of cultural diversity. Unlike the latter, but similarly to the TNM, the model dynamics goes through a series of metastable stages of increasing length, each characterized by mutually enforcing cultural patterns. These patterns are abruptly replaced by other patterns characteristic of the next metastable period. We analyze the time dependence of the population and diversity in the system, show how different cultures are formed and merge, and how their survival probability lacks, in the model, a finite average lifetime. Finally, we use historical data on the number of car manufacturers after the introduction of the automobile to the market, to argue that our model can qualitatively reproduce the flurry of cultural activity which follows a disruptive innovation.

1996

Culture is an evolutionary domain in which paradigms evolve through the replication and variation of memes and psychological traits. In biology genes flow in such a restricted way that there is a relatively transparent relationship between genealogy and taxonomy. In culture memes are borrowed freely between lineages so that a given paradigm may have contributions from many cultures. Further, under certain conditions cultures come into such intimate contact that the process of creolization produces new paradigms within a relatively few generations. Consequently cultural taxonomy is inherently more complex than biological taxonomy. Dynamically, over the long term culture exhibits an S-shaped growth curve which reflects the proliferation of memes within cultures. Perhaps the deepest issue in cultural evolution is the Gestalt switch which happens between the highest level of one cultural rank and the beginning of the next rank.

Complex Adaptive Systems Modeling

Purpose: Agent-based models are typically "simple-agent" models, in which agents behave according to simple rules, or "complex-agent" models which incorporate complex models of cognitive processes. I argue that there is also an important role for agent-based computer models in which agents incorporate cognitive models of moderate complexity. In particular, I argue that such models have the potential to bring insights from the humanistic study of culture into population-level modeling of cultural change. Methods: I motivate my proposal in part by describing an agent-based modeling framework, POPCO, in which agents' communication of their simulated beliefs depends on a model of analogy processing implemented by artificial neural networks within each agent. I use POPCO to model a hypothesis about causal relations between cultural patterns proposed by Peggy Sanday. Results: In model 1, empirical patterns like those reported by Sanday emerge from the influence of analogies on agents' communication with each other. Model 2 extends model 1 by allowing the components of a new analogy to diffuse through the population for reasons unrelated to later effects of the analogy. This illustrates a process by which novel cultural features might arise. Conclusions: The inclusion of relatively simple cognitive models in agents allows modeling population-level effects of inferential and cultural coherence relations, including symbolic cultural relationships. I argue that such models of moderate complexity can illuminate various causal relationships involving cultural patterns and cognitive processes.

Physica A: Statistical Mechanics and its Applications, 2013

We introduce a variant of the Axelrod model of cultural dissemination in which agents change their physical locations, social links, and cultures. Numerical simulations are used to investigate the evolution of social network communities and the cultural diversity within and between these communities. An analysis of the simulation results shows that an initial peak in the cultural diversity within network communities is evident before agents segregate into a final configuration of culturally homogeneous communities. Larger long-range interaction probabilities facilitate the initial emergence of culturally diverse network communities, which leads to a more pronounced initial peak in cultural diversity within communities. At equilibrium, the number of communities, and hence cultures, increases when the initial cultural diversity increases. However, the number of communities decreases when the lattice size or population density increases. A phase transition between two regimes of initial cultural diversity is evident. For initial diversities below a critical value, a single network community and culture emerges that dominates the population. For initial diversities above the critical value, multiple culturally homogeneous communities emerge. The critical value of initial diversity at which this transition occurs increases with increasing lattice size and population density and generally with increasing absolute population size. We conclude that larger initial diversities promote cultural heterogenization, while larger lattice sizes, population densities, and in fact absolute population sizes promote homogenization.

Electronic Proceedings in Theoretical Computer Science, 2009

There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper contains a very preliminary investigation of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. The ideal proportion with respect to fitness of ideas occurs when thirty to forty percent of the individuals is creative. When creators are inventing 50% of iterations or less, mean fitness of actions in the society is a positive function of the ratio of creators to imitators; otherwise mean fitness of actions starts to drop when the ratio of creators to imitators exceeds approximately 30%. For all levels of creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents.