The emergence of signaling systems has been observed in numerous experimental and real‐world contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they are usually framed as being mutually incompatible. Using an exemplar‐based framework, we replicate these models in a minimal configuration which allows us to directly compare them. This reveals that the development of optimal signaling is driven by similar mechanisms in each model, which leads us to propose three requirements for the emergence of conventional signaling. These are the creation and transmission of referential information, a systemic bias against ambiguity, and finally some form of information loss. Considering this, we then discuss some implications for theoretical and experimental approaches to the emergence of learned communication.

Like other socially transmitted traits, human languages undergo cultural evolution. While humans can replicate linguistic conventions to a high degree of fidelity, sometimes established conventions get replaced by new variants, with the gradual replacement following the trajectory of an s-shaped curve. Although previous modelling work suggests that only a bias favoring the replication of new linguistic variants can reliably reproduce the dynamics observed in language change, the source of this bias is still debated. In this paper we compare previous accounts with a momentum-based selection account of language change, a replicator-neutral model where the popularity of a variant is modulated by its momentum, i.e. its change in frequency of use in the recent past. We present results from a multi-agent model that are characteristic of language change, in particular by exhibiting spontaneously generated s-shaped transitions that do not require externally triggered actuation. We discuss several empirical questions raised by our model, pertaining to both momentum-based selection as well as other biases and pressures which have been suggested to influence language change.

Digital objects require appropriate measures for digital preservation to ensure that they can be accessed and used in the near and far future. While heritage institutions have been addressing the challenges posed by digital preservation needs for some time, private users and SOHOs (Small Office/Home Office) are less prepared to handle these challenges. Yet, both have increasing amounts of data that represent considerable value, be it office documents or family photographs. Backup, common practice of home users, avoids the physical loss of data, but it does not prevent the loss of the ability to render and use the data in the long term. Research and development in the area of digital preservation is driven by memory institutions and large businesses. The available tools, services and models are developed to meet the demands of these professional settings.This paper analyses the requirements and challenges of preservation solutions for private users and SOHOs. Based on the requirements and supported by available tools and services, we are designing and implementing a home archiving system to provide digital preservation solutions specifically for digital holdings in the small office and home environment. It hides the technical complexity of digital preservation challenges and provides simple and automated services based on established best practice examples. The system combines bitstream preservation and logical preservation strategies to avoid loss of data and the ability to access and use them. A first software prototype, called Hoppla, is presented in this paper.

The emergence of signaling systems has been observed in numerous experimental and realworld contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they are usually framed as being mutually incompatible. Using an exemplar-based framework, we replicate these models in a minimal configuration which allows us to directly compare them. This reveals that the development of optimal signaling is driven by similar mechanisms in each model, which leads us to propose three requirements for the emergence of conventional signaling. These are the creation and transmission of referential information, a systemic bias against ambiguity, and finally some form of information loss. Considering this, we then discuss some implications for theoretical and experimental approaches to the emergence of learned communication.