Time And Policy In The Information Society

Time and Policy in the Information Society Jesse MARSH Atelier Studio Associato, via XX Settembre 70, Palermo, 90141, Italy Tel: +39 091 6253378, Fax: +39 091 6253378, Email: [email protected] Abstract: Policy making is inevitably a function of temporality in many aspects. In the Information Society, the time-space for decisionmaking has changed notably with the increased pace of technological and social change. In addition, the reflexivity of world networks leads to almost real-time impacts of local events or crises. In short, policy making is now characterised by instability and uncertainty. This paper looks at the policy-making process in this context, examining approaches that try to address this new relationship to time. 1. Introduction Anyone with teen-age children knows that most video games are not (as, say, the game of chess) based on a fixed set of rules known to all players at the outset. The game rather consists in discovering the rules by trial and error. In our transition towards the Information Society, decision and policy-making appear to be undergoing a similar transformation. There is general agreement that decisions today are characterised by a context of instability and uncertainty: instability in the sense that, even while the game is being played, the rules of the game are changing; and uncertainty in the sense that expected outcomes may not occur after all. The question is: can policy actions be implemented before everything has changed again? That in turn is difficult to judge in the absence of a clealy structured model of policy-making with respect to the main variable in both innovation and uncertainty: time. This paper makes a first attempt to look at policy and time in this context. 2. Time and Policy-making 2.1 – Traditional policy-making procedures The practice of policy-making is generally embedded in constitutional and/or legal frameworks whose relationship to time is often taken for granted.1 We can identify some of the main phases in the policy-making process and variables affecting the duration of each: • Time to read the environment: this is an on-going process of understanding the dynamics of the context policy-makers operate in, generally carried out by the research community. • Time to define a strategy: this is the political framing of what to do, developed by strategic think tanks and refined by the research staff of parties and politicians; although this is also an on-going process the time required to respond to a specific problem depends on a series of political variables, primarily the clarity (and previous success) of the general political strategy. 1

  One well­known exception, for instance, is the electoral process for the President of the United States: an  Electoral College with representatives from each State was devised in part to take into account the time required to  reach the capital on horseback.

•

Time to build consensus: this is the more uniquely political phase of the process, and its fluidity is an important measure of success; it also coincides with the beginning of procedural time, as specific proposals for enactment by law are drafted. • Time to approve: this is in theory entirely procedural, although in practice it depends on more political factors such as agenda-setting as well as the level of consensus reached in order to avoid delays. • Time to finance: this phase can vary significantly according to whether the plan can rely on existing budgets, needs to draw on external or private financing, etc. (the “time of money” in the public sphere could be a research topic all to itself). • Time to implement: again, this is in theory a merely technical question; yet procedures and politics can also influence factors such as the time required to open and assign a tender. • Time to evaluate: this is a varied process occurring in parallel according to different criteria for success, i.e. if the policy was implemented, if it was effective, if it was efficient, etc.; in general, this process takes us back to the first step of reading the environment. It becomes clear that there is a mixture of technical, procedural and political factors affecting the time it takes to complete the policy-making life cycle. When policy-making appears to be ineffective, the consensus is that decisions are “too slow” or “taking too much time to enact”, but often the wrong factor is taken to blame. In a situation of friction between political allies, for instance, inevitably there is a call for reform of voting procedures. Yet there is an increasing doubt that it is neither the strategy nor the procedure nor the lack of consensus that is the problem, but the approach to time itself. 2.2 – Time in the Information Society In the Information Society, the pace of technological innovation and economic globalisation is continuously increasing, and the reflexivity of world networks leads to almost real-time impacts of local events or crises. These are all factors that deeply affect time and its relationship to policy-making. In terms of the pace of change, an increasing number of phenomena are obeying Moore’s Law. This was originally formulated with regards to micro-processors, predicting that capacity would double every two years. The shift from a “progressive” to an “exponential” model for the increase of the rate of change has been a shock for traditional planning in sectors such as engineering and marketing. The challenge of Moore’s Law to traditional policy-making procedures is that a process that is steady over time – implementing a decision – is superimposed on processes that are accelerating at an exponential rate, namely those that define the environment that the decision aims to affect. Yet even with Moore’s Law we are in a world of linear time, where things happen one after the other and the emphasis is on how much time it takes for a single process (i.e. the doubling of processing power) to complete its cycle. In parallel, complexity and chaos theory – identifying similar patterns in phenomena in a variety of fields from biology to economics – have made us also look closely at concurrent processes, or things that happen at the same time, or other situations where time is not simply a linear process that a clock can keep track of. Non-linear behaviour for instance has been seen to characterise phenomena from financial markets to voting preferences. More recently, network theory (notably with Barabasi) has developed to explain the behaviour of interconnected systems in relationship to the nature and pattern of links in the system. In this view, time is a relatively independent variable, as elements or sectors of a

network can develop, decay or collapse according to different rhythms. The emphasis shifts rather to the quality of states of equilibria, resilience to shocks, reconfigurability, etc. 3. Policy-making in the Information Society 3.1 – Linear and Developmental Decision Models If we return to the procedural model of decision-making processes, we can reduce it to a simple linear sequence. In a very straightforward manner, analysis of the environment provides the basis for policy decisions, which lead to action. Example: the economy needs jobs; we decide to build a dam; jobs are created. ENVIRONMENT

DECISION

ACTION

Linear Policy Model While this linear policy model may no longer work in fields characterised by uncertainty (e.g. job creation), it is still valid for some areas (e.g. providing rural areas with reliable electricity). We could generalise by saying that the linear model works in those fields where the timespan of change is significantly greater than the life-span of the policy intervention and where the environment we are acting on does not demonstrate complex systemic characteristics. A more reflexive variation – the so-called “developmental” policy model – introduces feedback mechanisms so that each of a set of cycles can “learn from” its predecessors. This is the case of large and complex programs such as the EU’s Framework Program for research, and also takes into account the need for cyclical political and financial accountability.

DECISIONS

ACTION

ENVIRONMENT

Developmental Policy Model In this case, we can generalise by saying that the developmental policy model works in those fields where the timespan of change is about equal to the life-span of the policy intervention. Indeed, the life-span of complex policy actions is often determined by a rough guess about the pace of change in the environment for the given field of action. 3.2 – Side-stepping sequential time The basic problem we defined at the outset however is that the pace of change has accelerated to an unprecedented degree. Calls to make the procedural policy-making

process more “efficient” by improving feedback or intervening on the time required to complete each step are therefore missing the mark. Opinion polls, improved assembly procedures, accelerated tender assignments, etc. may improve the efficiency of government but no longer provide a guarantee that policy-making will deliver more effective responses. Even the minimum time span for following the basic procedural route is by now simply too long. The late 1990s witnessed a series of experiments in “exploratory” policy-making within the European Union, shifting the emphasis on detail to actors on the ground. Early examples included: • The European Regional Development Fund, which covers a sizeable portion of the EU budget, set aside 1% of its 1994-1999 budget for experimental projects in fields such as inter-regional cooperation, Information Society initiatives, spatial planning, and culture. These programs, known as Article 10, primarily aimed to help the ERDF learn how to re-direct the priorities for the other 99% for the following cycle of funding. • DG XIII (Telecommunications, Information Market and Exploitation of Research) developed a tradition of launching “exploratory actions” which either led to full-scale programs or aimed to transversally influence the definition of future workplans. An early example was the Telework Stimulation Actions (1994-1995). • The Information Society Project Office (ISPO), established in 1995, originally had no budget of its own for launching projects. It’s purpose was rather to coordinate existing initiatives and existing sources of financing. A key part of this strategy was the constitution of the Information Society Forum, a consulting body for EU policy making consisting of high-level representatives “from all walks of life”. While some may argue that this trend avoids taking real policy commitments by leaving the hard choices in the hands of “field actors”, the real shift is in the primary output of policy-making. In the traditional model, governments “buy” action: they pay for it and in many cases actually carry it out.2 In the emerging model of policy-making, governments p ace the emphasis on “shaping” the actions of others. The direct output of policy-makers is no longer the action itself, but the set of images/representations, idee-fortes, and action frameworks which stimulate and give some broader coherence to the actions of “field actors”. A good example of this was US Vice-President Gore’s coining of the term “Information Superhighway”, which probably did as much for the spread of the Internet as the more concrete policies. The difference could be represented as follows:

BUY

DECISIONS SPHERE OF

IMAGE

FORMULATE

ACTION

DECISIONS

SHAPE

EVENTS

POLICY INTERVENTION

READ

ENVIRONMENT VERIFY

READ

ENVIRONMENT VERIFY

The Shift from Buy-oriented to Shape-oriented Policy

2

 Perhaps the clearest example of this is the U.S.’s program to land a man on the moon in the 1960s.

The important aspect of this approach is that it basically side-steps the linear model of time that defines the procedural policy-making process. The new emphasis is instead on “enabling” mechanisms that aim more at creating a “multiplier effect”3 than directly controlling implementation. Events no longer occur in sequence but they overlap if not occur simultaneously. Further, not all events (in the diagram, the change of label from ‘action’ to ‘event’ indicates a shift of stance) are necessarily within the sphere of government, with public and private resources often only loosely coupled to the specific policy initiative. 3.3 – Predicting the future If the output of policy has become an image or idee-forte which aims to shape external events, this means that we are practically moving from the realm of procurement to the dynamics of social systems. In this context, reading the environment is primarily a question of understanding the sets of rules (of which laws are only a part) which govern those dynamics. Formulating an image is therefore a question of predicting the impact of a possible future rule-set: we return to the video game example, where we basically build possible futures into the imagination of the present. If we decompose this in an analytical manner, the question of introducing time with respect to rule-sets means predicting a possible rule-set (t+1) as part of the decision. This can be illustrated as follows: R

PREDICT

VERIFY

D

READ

SHAPE

t+1

R t

R

t+1

E

MONITOR

The predicted rule-set (t+1) is what aims to determines the actual rule-set (t+1), which in turn feeds into the new rule-set (t), which is the starting point for a new cycle. A key feedback mechanism for policy-making, labeled ‘Verify’ in the above and preceding diagrams, enters into play to see if the future situation corresponds to what was predicted. This process is concurrent in time with the rest of the policy-making processes. This basic model gets more complicated when we consider that, by addressing a series of actors at different levels of responsibility, it is essentially fractal. If we take as an example European policy on SMEs, the rule-sets we would be looking at primarily concern the ways in which SME managers take decisions about, say, adopting e-commerce. The SME manager, however, also goes through the same process: imagining how the company might perform using e-commerce, attempting to guide new behaviour from employees (the SME manager can have a Web site designed but does not know how employees will use it), monitoring the new organisational patterns that emerge, and validating this with respect to the original goals. Perhaps we are back to the same trap of trying to compress time rather than re-think it. Indeed, if our European commissioner responsible for SME policy tries to take the SME 3

 

The concept at the basis of this principle is “leverage”, as developed by Peter Senge in The Fifth Discipline.

manager’s processes into account, he or she would need to consider an endless cascade of images and events but have to wait too long to verify them. From a political standpoint, the main issue remains the policy-maker’s ability to control outcomes or at least appear to have a grip on the processes under control. Ironically, the attempts to develop policy-making tools that provide better control over events continue to shift control of action into the hands of external actors. A somewhat desperate reaction to this paradox is the current proliferation of milestones and benchmarks, as with the eEurope 2005 initiative. Upon reflection, these fixed targets are in contradiction with the very nature of pre-emptive policy-making. 4. Conclusion: Towards viral policy-making? Perhaps the answer is instead to be found in approaches that emphasize the qualitative rather than the quantitative nature of both policy and time. The fractal paradox described above can be addressed by shifting attention to the pre-emptive decoding of others’ images of possible action, which takes us entirely into the sphere of reading, formulating and shaping lifestyles. Although such an approach has yet to be fully applied to policy-making, it corresponds to the viral model of propagation based on network theory. This model has been first used by the hacker community, later adopted by no-global and social movements with unexpected success and in parallel taken up as marketing hype during the dot.com bubble. While the marketing dimension needs to be clearly distinguished from the political and public service character of policy-making, these models do have a deep ethical foundation in their original formulations. Similar tools used for the promotion of consumption-oriented lifestyles can indeed be applied for the promotion of patterns of behaviour that benefit the community as a whole. A key feature of viral models is trust as a feature of a network link, where different network configurations (with different types of weak and strong links) can propagate desirable phenomena in different ways. Time can be thought of in qualitative terms in relation to trust: the quality, intensity, and frequency of relationships all determine a link’s strength and thus its role in a network. Considering networks as the basic terrain on which propagation-oriented policy-making could work, future experimentation would need to examine not only how desirable effects can be best carried throughout a network, but also develop purposive network building as an on-going area for policy intervention. In order to do so, however, it is also necessary to further investigate the temporal dimension of networks and the nodes and links that constitute them. References 1. Barabasi, A-L, Linked: The New Science of Networks, Perseus Books Group, 2002. 2. Cave, J., “Economic Shocks”, in The SASKIA Landing Place, the SASKIA Project IST2002-38184, www.is-sd.org 3. European Commission, eEurope 2005, http://europa.eu.int/information_society/eeurope/2005/text_en.htm 4. European Commission, ERDF Art. 10, http://www.inforegio.cec.eu.int/art10/ 5. European Commission, First Annual Report on Telework, http://www.etw.org/2003/Archives/Twk_95.pdf 6. European Commission, Information Society Project Office, http://europa.eu.int/ISPO/Welcome.html

7. European Commission, White Paper. on growth, competitiveness, and employment: The challenges and ways forward into the 21st century, COM(93) 700 final. Brussels, 5 December 1993. 8. Gleick, J., Chaos: Making a New Science, Penguin, 1987. 9. Himanen, P., The Hacker Ethic and the Spirit of the Information Age, with a prologue by Linus Torvalds and an epilogue by Manuel Castells, Random House, New York, 2001. 10. Jurvetson, S. and Draper, T., Viral Marketing, Netscape M-Files, 1997, currently available at http://www.dfj.com/files/viralmarketing.html 11. Klein, N., No Global: taking aim at the brand bullies, Knopf Canada, December 1999. 12. Marsh, J., Cultural Diversity and the Information Society: Policy options and Technological Issues, PE 297.559/Fin.St., Brussels, 2001. 13. Moore, G., “Cramming more components onto integrated circuits”, Electronics, Vol. 38, N. 8, April 19, 1965. 14. Paquot, T., The Art of the Siesta, Marion Boyars Publishers Ltd., 2003. 15. Raymond, E., The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary, O’Reilly, Sebastapol California, 2001. 16. Rifkin, J., The Age of Access: the new culture of hypercapitalism, where all of life is a paidfor experience, J.P Tarcher/Putnam, 2000. 17. Senge, P. The Fifth Discipline: The Art and Practice of the Learning Organization, Doubleday Books, 1990.