Natural and non-natural information and smart niches

December 3, 2018

The distinction between natural and non-natural information is highlighted by Laurence Kirmayer, that on my opinion helps to define the smartness concept in modern artificially enhanced systems. Particularly smartness is created by a loop – from self-organised individuals’ meanings, values and actions the data are aggregated and patterned using algorithms to form collective knowledge, and then these data are offered back as new affordances, action cues for the individuals. The human action niches are based on developing non-natural information cues and affinity to certain cultural memberships.

Grice (1957) distinguished between natural and non-natural forms of meaning, emphasizing the latter in most of his work. Natural meaning is a relation between two things that are correlated. Smoke ‘means’ fire because tokens of smoke reliably correlate with tokens of fire. Similarly, (certain kinds of) spots mean measles (understood not as the popular category but as the biomedically recognized infection with a particular virus). Non-natural meaning instead depends on the capacity of individual agents to exploit explicit and implicit social‘ conventions’(in the wide sense of locally shared norms, values and moral frames, expectations, ontologies, etc.) to infer the intentional states of other agents and thereby engage them or engage aspects of the environment with them. Red traffic lights, in virtue of convention (and law), ‘mean’ stop, and hence afford (and mandate) stopping — and this is made possible by the specifically human mastery of recursive inferences, both explicit and implicit, that agents make about other agents (Tomasello, 2014).

Different kinds of information (Piccinini, 2015; Piccinini & Scarantino, 2011; Scarantino & Piccinini, 2010).

‘Natural information’ obtains when a token informational vehicle x of kind X (that is, a sign, a pattern of neural activation, or what have you) carries natural information about some information source y of kind Y just in case there are reliable correlations between X and Y . Natural information, in other words, cannot misrepresent, for it is non-semantic; it is not the kind of thing that can be simply true or false.

‘non-natural information’ (or as we prefer to put it,‘conventional information’), pertains to semantic, content-involving representations that depend on social norms and cultural background knowledge. Non-natural information allows an agent to make a correct inference about some aspect of an intentional system, e.g., other agents, language and other symbolic systems such as mathematics, etc. Non-natural information is semantic in that it obtains in virtue of satisfaction conditions (e.g., truth conditions).

Kirmayer notes: “To operate with conventional affordances, agents must have shared sets of expectations — we must know what others expect us to expect.”

So smartness is not unconditional smartness but dependent of cultural constraints, it is a niche.

From https://www.academia.edu/26727261/Cultural_affordances_Scaffolding_local_worlds_through_shared_intentionality_and_regimes_of_attention


Quieting the Niche – the withdrawal from the space of commons

December 3, 2018

Finn Brunton and Helen Nissenbaum have addressed the methodology of countersurveillance to digital surveillance by developing the notion of ‘obfuscation’, a means of neutralising corporate surveillance via data collection and analysis, whereby individuals utilise certain methods to—using an analogy from cybernetics— introduce ‘noise’ into their signals, in order to produce “misleading, false, or ambiguous data to make data gathering less reliable and therefore less valuable.” In the digital domain this involves reducing variety, concealing knowledge and intention, and evading identification and accountability, behaviours counterindicated by the necessities of maintaining cooperative epistemologies. The withdrawal from the space of commons and adversarial refusal of participation in the construction of the niche further erodes our collective ability to share, contribute to, and harness the systems of epistemically mediating technologies that underpin out collective systems, our cooperative cultures, and our ability to face the chance and uncertainty of the future.
The ethical and epistemological problems emerging as a result of the particular environmental affordances of ICT platforms are not simply a question of how we ought to use them, but are the real, systemic, ecological constraints on an environment that will determine the character and variety of possible modes of interaction.

From: Quieting the Niche: on Dataveillance and Everyday Resistance by Sam Forsythe


Sustainability of smart digitally enhanced learning ecosystems

August 20, 2018
We have published an article that models sustainability issues in digitally enhanced schools. The digital innovation sustainability bottlenecks in schools are related with actuating the organizations as systems, that is influenced by ill-developed feedback loops of data-, learning- and information flows for the purpose of intra-organizational innovation driven transformation as well as for transforming the external socio-technical regime to make it more fit for the innovative schools’ needs.
Sustainability 2018, 10(8), 2672; doi:10.3390/su10082672
Smart, Digitally Enhanced Learning Ecosystems: Bottlenecks to Sustainability in Georgia
Eka Jeladze, Kai Pata

This paper stems from the need to identify the sustainability bottlenecks in schools’ digital transformation. We developed the conceptual model of the smart, digitally enhanced learning ecosystem to map transformation processes. We posit that the notion of sustainability is central to conceptualize learning ecosystems’ smartness.
The paper presents the mapping results of Georgian public schools’ data using the interviews from 62 schoolteachers, ICT managers, and school principles. The qualitative content analysis revealed that even the schools with comparative digital maturity level could not be considered as smart learning ecosystems that are transforming sustainably. The findings call for the design of technology integration in the school as a dynamic transformation that balances two sustainability intentions—to stabilize the current learning ecosystem with its present needs, while not compromising its pursuit to test out possible future states and development towards them.
We suggest schools build on the inclusion of different stakeholders in digital transformation; nourishing their resilience to ruptured situations; widening the development, testing, and uptake of digitally enhanced learning activities; weaving internal networks for sharing new practices; conducting outreach to change the socio-technical landscape; and developing feedback loops from learning, data, and information flows to manage the changes.

Factors Determining Digital Learning Ecosystem Smartness in Schools

April 27, 2018

It has taken a while to jointly prepare an empirical data-collection in three countries from different economic background together with my two PhD students James Quaicoe and Eka Jeladze.

Based on the observation grid to investigate schools as learning ecosystems the unified dataset was formed to answer the questions i was interested in how digital learning ecosystem functions according to the ecological principles.

The paper is now out at journal:


Open Society Technologies

April 12, 2018

Open society technologies

In the XXI Century societies are becoming more open – balancing between choices of top-to down and bottom up, individual and collective, passive responsibility and proactive entrepreneurship, exercising tolerance, inclusion and democracy.

Politico-legal dimension of open society tries to channel persons’ political agency and spreading power, moving the public from responsive to proactive behaviours and political activism. For example, Public Administrations – both in the national and in the local levels – are promoting citizen participation at community activities and civic initiative.

The open society would keep no secrets from itself in the public sense, as all are trusted with the knowledge of all. This brings us to the open data and personal data privacy issues – data and trust have became the commody of open societies. The eight most frequently stated public service values are: impartiality, legality, integrity, transparency, efficiency, equality, responsibility and justice.

Surveillance of people in the digital society is becoming an issue, especially that it was often established without public debate. Many new media technologies serve both as a tool for organizing public commons and as a tool for surveilling private lives. For example in China social credit’ system is applied that analyses internet shopping and social media use in order to blacklist ‘lazy’ or wasteful citizens and allow those who behave well to borrow money.

While exercising open politico-legal dimension of open society at technology level, the dilemmas of social justice have to be solved at algorithm levels run by technologies – for example how we can divide the public goods with social algorithms. The studies of Lee and Baykal ( 2017) indicate that even algorithms mathematically proven to be “fair” may not achieve “fair” social division from human perspectives, and furher studies in algorithmic transparency and accountability are needed among public. For algorithmic mediation to be fair, algorithms and their interfaces should account for social and altruistic behaviors that may be difficult to define in mathematical terms.

Governments can solve wiked dilemmas dividing social goods and bads (such as where to open a factory or mine) using algorithms, that locate stakeholders in a problem on a social network and calculate their benefits so that nonrival, heterogeneous benets for each other will be established (see Elliott & Golub, 2017).

Second rapidly increasing governing technology is nudging people. For example (Sunstein et al., 2017) found that people around the globe generally approve nudges for governmental information campaigns, mandatory information disclosure imposed by governments. For nudges about default rules disapproval is higher. Majorities disapprove subliminal advertising and mandatory choice. New governing behaviours are also related to digital nativeness of people – younger people are more likely than older people to approve of more intrusive interventions (such as manipulative messages and default rules). However, political attitudes were found to have only a modest effect on approval rates of nudges.

Socio-economic dimension of open society prompts for thinner state at service level and reducing state intervention, making individuals less dependent upon the state, mobilizing them and building on their responsive social entrepreneurship by increasing community self-help. There is need to give back to the public sector” the our collective potential for governing and valuing our own resources – as it was maintained in the tribal times for commons goods. Governing socio-economic dimension of open society requires managing the services ecosystem – providing public goods (education, parks, roads, public safety, sanitation, utilities, legal systems and national defense provided by sovereign governments); estabishing fair access to the commons/common goods – the shared resources which people manage by negotiating their own rules through social or customary traditions, norms and practices; empowering the social goods provided by social entrepreneurship; and critically examining end making use of the open goods accessible through business activities. At technology level we are talking about open service ecosystems where citizens can get services seemlessly from state as well as other providers. The discoverability, connectedness of such services are critical in order to be cost-effective and avoid overlaps but simultaneously being inclusive at service level, and not overlooking the needs of different small groups’ needs. For example, Estonia has set a target for providing country as a service.

Citizens and small and medium companies are increasingly willing to participate as they became conscious of their key impact in the public life. Citizen Science as a new research inclusive way for channelling social activism is to be scaled up in open societies, we need technologies and ways of letting the people to make say of what innovations we should fund in the society, and what is the impact of radical socio-technical changes to their life at global and local level.

Political freedoms and human rights are claimed to be the foundation of an open society. The socio-cultural dimension of open society exercises tolerance and democracy in interaction between the public sector activities and individual people’s voluntary activities and their self-development, responsible consumption and environmental responsibility, establishing cohesion and inclusion. Technology plays a key role to enable this citizen participation and exercising social justice, such as inclusiveness at data level used in future intelligent decision support systems and how we open the data for people. People in open society must have technologically enhanced ways of taking the critical frame of mind in the face of communal group think, and staying in the Filter Bubble.  


As a collaborative effort we are launching at Fall 2018 the new master programme Open Society Technologies (taught in English) in Tallin University. The programme will bring together the expertise from eGovernance, Human Computer Interaction, Social Innovation and Digital Technologies to open up the black box of how to develop an Open Digital Society. Our mission with Open Society Technologies curriculum is developing new professionals like open society system architects, analyst, software developers or development managers, gardeners of cyber-physical systems in open society, choice architects, community technologist, digital policy advisors in society, social entrepreneurs  etc. able of maintaining the dimensions of open society.

Further reading:

Principles and values of good governance. http://ec.europa.eu/esf/BlobServlet?docId=13956&langId=en

Building Public Trust: Ethics Measures in OECD Countries (2000) http://www.oecd.org/mena/governance/35527481.pdf

REAL BLACK MIRROR (2018). https://www.thesun.co.uk/news/5730910/china-social-credit-rating-blacklists-citizens/ 

Sheperd, S. (2015). Surveillance of digital life and the use of sousveillance as a response https://medium.com/@sam.shepherd/surveillance-of-digital-life-and-the-use-of-sousveillance-as-a-response-7b306cfdb6e8

Lee, M. Baykal, S. (2017). Algorithmic Mediation in Group Decisions:Fairness Perceptions of Algorithmically Mediated vs.Discussion-Based Social Division https://www.cs.cmu.edu/~mklee/materials/Publication/2017-CSCW-AlgorithmicMediation_Fairness.pdf

Elliot, M Golub, B. (2017) Network approach to public goods. http://www.people.fas.harvard.edu/~bgolub/papers/jmp.pdf

Fetherson et al. (2017). The Persuasive Nudge Power https://www.bcg.com/publications/2017/people-organization-operations-persuasive-power-digital-nudge.aspx

Sunstein C.S et al. (2017). Behavioral Insights All Over the World? Public Attitudes Toward Nudging in a Multi-Country Study https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2921217

Government by nudge http://bigthink.com/Mind-Matters/government-by-nudge-is-a-global-phenomenon

The nation that thrieved by nudging its people http://www.bbc.com/future/story/20180220-the-nation-that-thrived-by-nudging-its-population

Milard, J., Carpenter, G. (2014) Digital technology in social innovation. TEPSIE is a research project. http://www.transitsocialinnovation.eu/content/original/Book%20covers/Local%20PDFs/124%20TEPSIE%20synopsis%20digital%20technology%20in%20SI.pdf

Maiolini et al., (2016). Digital Technologies for Social Innovation: An Empirical Recognition on the New Enablers https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-27242016000400004

Kotka, T. (2016). Country as a service. https://e-estonia.com/country-as-a-service-estonias-new-model/

EGOVIS 2016. Book Electronic Government and the Information Systems Perspective  https://link.springer.com/book/10.1007%2F978-3-319-44159-7

EGIVIS 2017. Book Electronic Government and the Information Systems Perspective http://www.springer.com/gp/book/9783642151712

Digital democracy https://www.nesta.org.uk/report/digital-democracy-the-tools-transforming-political-engagement/


Artificial intelligence for social good https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf


Citizen science in schools

April 3, 2018

Esitlus Reaalkoolis kodanikuteadusest.





Survey instrument: digital workers’ preferences of informal learning opportunities in socio-technical learning ecosystem

January 12, 2017

The survey was developed based on the informal learning interactions in workplaces described by Ley and associates [2014]. The survey items elaborated possible socio-technical system functionalities using the ideas from the prototypes of Learning Layers tools. The online survey comprised of three groups of statements that represent the socio-technical learning system dimensions for informal learning at work:

  1. Sensemaking statements: Learn & organize knowledge (11), Share knowledge (5), Annotate knowledge (5)
  2. Scaffolding statements: Search Resource (3), Find Resource (2), Awareness of resources (5), Find expert (4), Share help requests (2), Get expert Guidance (6)
  3. Knowledge maturing statements: Accumulate knowledge in system (5), Co-construct knowlede (7), Validate resources and experts with technology means (5)

ANNEX. Survey: Socio-technical learning ecosystem opportunities for informal workplace learning

Learn and Reorganize knowledge

  • I find it useful identifying learning needs at work using the computer support
  • I find it useful revisiting the exciting learning moments later on
  • I find it useful taking records (notes, memos, reminders, photos, videos etc.) to capture my learning moments at work
  • I find it useful that learning records captured at work could be used for further learning.
  • I find it useful organizing the records of my learning moments into meaningful learning episodes
  • I find it useful making records of which tools/resources I have used at work
  • I find it useful reflecting (writing, audiotaping etc.) about learning records to make sense of what was learned
  • I find it useful organizing records of learning into personal portfolio
  • I find it useful collecting into personal portfolio learning resources about interesting topics
  • I find it useful composing different views of records in portfolio for different purposes.
  • 5I find it useful learning from videos of good practice and failure created by others

Annotate knowledge

  • I find it useful adding keywords/notes to my learning records
  • I find it useful organizing learning records/resources with tags/keywords suggested by the system

Share knowledge resources

  • I find it useful that my reflections about learning will become part of shared resources
  • I find it useful that author can decide the access and sharing rights for each record in the personal portfolio
  • I find it useful that each document could be shared with others for learning purposes
  • I find it useful sharing documents/folders with other professionals for learning
  • I find it useful sharing documents with other professionals across workplaces

Search knowledge resources

  • I find it useful searching the latest information about the topics of my learning interests
  • I find it useful using mobile devices for searching learning materials directly at work
  • I find it useful searching suitable learning materials from the shared system

Find knowledge resources

  • I find it useful finding learning materials related to my work easily during working
  • I find it useful to access my previous learning records when I need them during work

Awareness and recommending

  • I find it useful to get automatically notices about shared resources and learning activities of other professionals in my field
  • I find it useful to get automatical notices about the modifications of certain normatives or guidelines
  • I find it useful discovering new learning interests by getting notifications of learning interests and needs of others
  • I find it useful getting system suggestions of the most relevant learning materials that other users have considered useful
  • I find it useful using guidance materials created by other learners

Find expert

  • I find it useful expanding social networks with new experts
  • I find it useful of requesting help from my social network at work
  • I find it useful identifying trustful learning experts by their rank of the quality of help they have provided
  • I find it useful getting suggestions to expand my social network with relevant experts who can provide guidance

Get expert guidance

  • I find it useful negotiating problem/task context while receiving/providing guidance
  • I find it useful getting less guidance when competence increases
  • I find it useful mainly receiving guidance how to better organize my learning activities at work
  • I find it useful mainly receiving hints how to make sense of new knowledge in work context
  • I find it useful being guided by experts in using collective resources
  • I find it useful being guided by experts in using the objects and tools at work

Share help requests

  • I find it useful seeing the help requests from others that match with my expertise
  • I find it useful sharing the help requests in my social network to locate most relevant experts


Co-construct knowledge

  • I find it useful co-constructing new learning resources from different people’s contributions
  • I find it useful that learning resources can be improved by incorporating different viewpoints from experts
  • I find it useful that learning resources can be improved by integrating related resources
  • I find it useful improving official descriptions of work processes, normatives and guidelines by local networks of experts
  • I find it useful discussing normatives and guidelines locally among experts
  • I find it useful creating knowledge of work processes as a result of many contributors‘ efforts
  • I find it useful collecting knowledge of good guidance and support from actual guiding practices at workplaces

Validate with technology means

  • I find it useful that other professionals in the network can rate learning resources
  • I find it useful that other professionals in the network can endorse my competences
  • I find it useful endorsing personal expertise by networking peers
  • I find it useful rating or commenting learning materials from my task context to make them better contextualized
  • I find it useful rating experts based on provided guidance

Accumulate knowledge

  • I find it useful that everyone’s learning events can be automatically traced
  • I find it useful that each tool and learning material has digital records and use-histories.
  • I find it useful that digital documents would capture discussions about learning episodes around them
  • I find it useful that learning resources can collect discussions about them
  • I find it useful that learning resources can be improved by accumulating their use-histories
  • I find it useful that normative guidelines at work would consist of ‚official‘ immutable and ‚inofficial‘ mutable content
  • I find it useful influencing the collective knowledge by personal notes
  • I find it useful accessing the use-histories of objects, tools or digital learning resources