IEEE Technology and Society Magazine - Spring 2014 - 29

as the inputs to a forgiveness decision, and used fuzzy
logic to implement this model. This system was then
able to distinguish between intentional and unintentional violations, gradations of seriousness, and distinguish between "risk" trust (first encounter) and
"reliance" trust (a shortcut based on prior experience).
The critical aspect of this forgiveness model is that
some of the constituent signals, especially "prior beneficial relationship," are a form of social capital. They
can be quantified but the reasoning with those quantities was entirely based on subjective assessments
using fuzzy, context-sensitive reasoning.
Legitimate Claims
It is a commonplace occurrence, in open distributed
computer systems and networks, for a set of autonomous
components to have to pool their resources so that as a
group they can achieve collective outcomes that they
could not achieve acting individually. This mirrors the
situation often facing a group of human actors: how to
share and maintain a common-pool resource, e.g., water
for irrigation, fisheries, forestry, grazing land, and so on.
Given a set of pooled resources and a set of actors
(agents) requiring access to the resources, there are a
number of "natural" solutions for determining who gets
access: free-for-all, pecking order, "form an orderly
queue," etc. Ostrom [9] studied how human societies
formed self-governing institutions, formed by people
willing to self-regulate the provision and appropriation
of resources according to mutually-agreed, conventional rules. This study was particularly concerned with
discriminating between those institutions that endured
and sustained the resource over time (avoiding the socalled tragedy of the commons), and those that did not.
Ostrom then specified eight institutional design principles that, from one perspective, are concerned with
establishing the essential and determinate conditions
for "forming an orderly queue."
However, while the design principles focused on the
conditions for forming the queue, they necessarily made
some assumptions about the properties of the queue
itself, in particular whether the distribution of resources
achieved by the queue was, in some sense, fair. This
requirement for distributive justice has been studied in
many fields, and rescher [10] proposed a theory based
on the idea of legitimate claims. rescher held that all the
various mechanisms for distributing resources could be
categorized under one or other of seven different canons.
His position was that each canon could be seen as representing a claim for access to resources; and that distributive justice consisted of determining, for any particular
context, what the legitimate claims were, how to accommodate multiple claims in the case of plurality, and how
to reconcile them in case of conflict.
In [11], rescher's theory of distributive justice was
formalized in the context of Ostrom's institutional
IEEE TECHNOLOGY AND SOCIETY MAGAZINE

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SprING 2014

design principles, specifically the principle of collective choice arrangements (those affected by provision
and appropriation rules should participate in their
selection and definition). Each of the canons was represented as a function that computed an ordering of
the agents requesting resources. The functions were
then used in a weighted Borda Count voting protocol
that computed an overall order. To reconcile conflicts
between claims, the agents themselves decided the
weight to be associated with each function.
Experimental results showed that groups of agents
implementing this allocation procedure achieved fairer
distributions than alternative random, rationing, or queuing schemes. However, in the current context, the key
point to note is that the representation of some of the
claims - notably the claims according to efforts and sacrifices, and according to socially-useful services - provide a ranking based on a form of (earned) social capital.
Demand-Side Self-Organization in SmartGrids
The traditional model of electricity generation, as generally experienced by domestic consumers, has supply
follow demand (with some minor variations, e.g., spot
market, day-ahead market, and so on). The essence is
the same: actual or predicted demand is determined
and supply (generation) is scheduled and produced
to satisfy that demand. The model has worked well
enough until now, but there are various developments
that are disrupting this model: over-provisioning of
generation to accommodate peak demand is inconsistent with reduced carbon emissions, local generation by domestically-installed solar panels, and the
proliferation of programmable "smart" devices (centrally scheduling the few devices of the early adopters is manageable; scheduling millions of devices as
"smart" devices become mainstream is not).
This has led to an increasing focus on demandside management for electricity markets [12], and
mounting emphasis on the involvement of consumers through active participation or user engagement.
In addition, domestic consumers have experienced
increased deployment of so-called "SmartMeters," an
ICT-enabled device installed "at the edge" of the electricity network. These devices are capable of monitoring and reporting electricity consumption from the
meter to the central system, as well as accepting control signals in the other direction.
There has been (at least anecdotally) some resistance to the introduction of SmartMeters in domestic
residences, as opposed to the enthusiastic adoption of
Smartphones. Arguably, the reason for this contrast is
because the latter is (mostly) an opt-in technology owned
by the end-user which facilitates generativity (the innovation of new tools from old ones, not perhaps imagined
or intended by the innovator of the old tools). However,
the SmartMeter is a "can't-opt-out" technology both
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