Initiating organizational memories using ontology network analysis

One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda

Naturalizing institutions: Evolutionary principles and application on the case of money

In recent extensions of the Darwinian paradigm into economics, the replicator-interactor duality looms large. I propose a strictly naturalistic approach to this duality in the context of the theory of institutions, which means that its use is seen as being always and necessarily dependent on identifying a physical realization. I introduce a general framework for the analysis of institutions, which synthesizes Searle's and Aoki's theories, especially with regard to the role of public representations (signs) in the coordination of actions, and the function of cognitive processes that underly rule-following as a behavioral disposition. This allows to conceive institutions as causal circuits that connect the population-level dynamics of interactions with cognitive phenomena on the individual level. Those cognitive phenomena ultimately root in neuronal structures. So, I draw on a critical restatement of the concept of the meme by Aunger to propose a new conceptualization of the replicator in the context of institutions, namely, the replicator is a causal conjunction between signs and neuronal structures which undergirds the dispositions that generate rule-following actions. Signs, in turn, are outcomes of population-level interactions. I apply this framework on the case of money, analyzing the emotions that go along with the use of money, and presenting a stylized account of the emergence of money in terms of the naturalized Searle-Aoki model. In this view, money is a neuronally anchored metaphor for emotions relating with social exchange and reciprocity. Money as a meme is physically realized in a replicator which is a causal conjunction of money artefacts and money emotions. --Generalized Darwinism,institutions,replicator/interactor,Searle,Aoki,naturalism,memes,emotions,money

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

How is the relationship significance brought about? A critical realist approach

The markets-as-networks theorists contend, at least tacitly, the significance of business relationships for the focal firm – that is, business relationships contribute somewhat to the focal firm’s survival and growth. We do not deny the existence of significant business relationships but sustain, in contrast to the consensus within the Markets-as-Networks Theory, that relationship significance should not be a self-evident assumption. Significance cannot be a taken-for-granted property of each and every one of the focal firm’s business relationships. We adopt explicitly a critical realist position in this conceptual paper and claim that the relationship significance is an event of the business world, whose causes remain yet largely unidentified. Where the powers and liabilities of business relationships (i.e., their functions and dysfunctions) are put to work, inevitably under certain contingencies (namely the surrounding networks and markets), effects result for the focal firm (often benefits in excess of sacrifices, i.e., relationship value) and as a result the relationship significance is likely to be brought about. In addition, the relationship significance can result from the dual influence that business relationships have on a great part of the structure and powers and liabilities of the focal firm, i.e., its nature and scope respectively.Markets-as-Networks Theory, relationship significance, business relationships, focal firm, resources, competences, activities

Human metabolic adaptations and prolonged expensive neurodevelopment: A review

1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 
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Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Innovation in the collective brain

Innovation is often assumed to be the work of a talented few, whose products are passed on to the masses. Here, we argue that innovations are instead an emergent property of our species' cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains. We outline how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are serendipity, recombination and incremental improvement. We argue that rates of innovation are heavily influenced by (i) sociality, (ii) transmission fidelity, and (iii) cultural variance. We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient. We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences and the dramatic increase in IQ test scores over time

Good decisions : reconciling human rationality, evolution, and ethics

Cover title.Includes bibliographical references (p. 29-35).by Steven F. Freeman