Designing experienced simultaneity: a new approach for research by design
Walter Dejonghe
Industrial Design Center, HOWEST - University Ghent.
Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
Walter.Dejonghe@Ugent.be
This publication is based on a new paradigm: the experience of time is an aspect of reality that is created by the impossibility for a particular agent in a particular context to experience two aspects “at once” or “simultaneously” and this can be designed because agents and their contexts can be designed. The major characteristics of this new paradigm are developed from very basic assumptions using an experimental set-up with quantified time measurements. Gradually this is clarifying how simultaneities can be designed by a team in a particular context, designing more simultaneities in a given lapse of time, what could be understood as the design of the form of time. It is argued that these design actions are not different from the design of new products, tools and processes and that a team with the highest organisation (including its tools, instruments and environment) will always achieve more simultaneously and thus achieve an expected outcome in the shortest time. The study of simultaneities, the basis of the new paradigm, opens a new field of research by design.
Keywords: research by design, experience, simultaneity, time
For the existing scientific paradigm, the design of the form of time is impossible. However: relativity theory shows that space cannot be interpreted as the union of mutually excluding positions independent of what happens in it, thus also time cannot be interpreted as the union of mutually excluding moments independent of what happens in it, and the resulting new entity “space-time” cannot be interpreted as the union of mutually excluding events, independent of what happens in it. There seems to be an aspect of space-time that depends on that what happens in it. Simultaneity is thus operationally questioned in relativity theory. In quantum theory, quantum processes also occur “non-local” and “outside of time” and this theory also is well grounded operationally. For the existing scientific paradigm both front sciences however are seen as weird effects of singularities, of no use in daily reality. The mystery still rules.
Moreover: most books and papers on time start with a famous quote: “What then is time? If no one asks me, I know what it is. If I wish to explain it to him who asks, I do not know. (Saint Augustine, Confessiones XI, 14, 17, 354–430 C.E.)” and than turn to the scientific paradigm as usual, accepting a linear aspect of time only.
I cannot disagree more with Saint Augustine, and I can imagine that in his world-view he really had a problem. If one grounds ones world-view operationally however, a time-lapse (!) is experienced (observed, measured, ...) by an agent whenever it is impossible for that agent to simultaneously experience at least two events. This sounds like a tautology, but this means that time is an aspect of reality that is created by the impossibility for a particular agent in a particular context to experience two aspects “at once” or “simultaneously”: for the agent both aspects (called events) are mutually exclusive, and indeed the concept “event” implies a space aspect and a time aspect. In other words: the experienced time by the agent depends on the agents local (context dependent) possibilities. Agents possibilities are context dependent, and for most agents possibilities are created and evolve when agents are evolving. Thus: experienced time is changing when agents are evolving. This can be defined operationally, so there is no mystery: operationally grounded time is agent-dependent and context-dependent and thus bounded. There is no need for an absolute definition of time. The time of (locally or globally) acting agents can operationally be investigated without the need for an absolute definition, let alone a linear one. Time inherently is a very specific difference (!) between two aspects of reality that are impossible to experience simultaneously and this depends on specific contexts and capacities. This can be defined operationally by the mutual exclusivity of two (!) aspects.
In this publication I argue that the undertaking of developing a new paradigm for time, and the resulting field of research by design, is worth while. I therefore present an experimental set-up with quantified time measurements showing how (the experience of) time can be designed. One of the practical consequence is that this model also explains why some teams are more performing than other teams, suggesting a method to increase the performance of teams. But more there is: I argue that the form of time can be designed by creating new agents, or by evolving some existing agents, and that this design action is not different from the design of new products, tools and processes.
I use in the proposed model of the new paradigm no exotic features, I even use as the experimental goal the most common known personal pursuit of gain (or avoidance of loss). To fully understand “design” in a time setting I propose the notion of simultaneity instead of the notion of form. Both are information-related concepts. I start to build the model with very well known concepts of activity distribution, planning etc... and I gradually expand the discussion. I end by relating this model to new concepts: the relation of simultaneity and the concept of mutually excluding states that are operationally and formally developed in my other (yet unpublished) papers founding and expanding this new paradigm.
The purpose of the model is to investigate a few design possibilities for agents trying to reduce the time it takes to carry an activity to a desired end. In the model we want to give a picture as realistic as possible. We achieve this because we give a well known ground for the reduction of the time: the personal pursuit of gain (or avoidance of loss) for a quantitative outcome that can be distributed among the agents (for example positive and negative values of any dimension, for example yields and costs, profits and losses, ...). In the model we will distribute the outcome inversely proportional to the time spent by the activity because we aim to investigate the possibilities for a reduction of the time spent.
To measure what happens in the model we will measure the following parameters, and these measurements are clearly defined and realistic:
· Who performs what (this easily can be measured by a local comparison of the transaction of activities: when some agent has taken over some results of an activity, the previous agent has ended this activity).
· How much time is spent. Time is measured by any local circular process which activity runs simultaneously with the investigated activity. In this publication all uses of the word “time” is meant as “local time”. A circular process is anything with at least two mutually excluding reoccurring states. We count the amount of reoccurences of one of the states and use this number as time measurement. Observe that this is a difference, not an absolute value, the zero point is arbitrary. Local time-spans are recorded at the moment of the transaction process. Transaction time is measured at the receiver using the local time model. Local time-spans are added up using conversions that are agreed upon (part of the coordination model that all agents share). There is no need for a global time model. There is no need to accommodate for communication time (this is explained later).
To understand the model we have to make a distinction between activities with a goal and activities without a goal (activities with limits and without limits).
Although it is impossible to have a complete model for what we are doing, for some activities we have a model of what should be performed, or what should be the outcome or the goal of the activity. This model is available before we start the activity (and eventually (hopefully) agreed to by all colleagues) and it is used during the process as measuring instrument: we compare what “should” and what “is”. Based on the difference the process is adapted (eventually held). The model of the outcome of the activity can be available during the whole process in several symbolic or conceptual forms. Their generic form is the following: with every action during the process we simultaneously experience the possibility of the outcome. Indeed, because we have that model available, we continue the process as long as the outcome is still possible, and we recognize the outcome when we do achieve it. More precisely: we recognise the outcome of an activity, when the real outcome simultaneously realises also the model of the outcome. And again: not only then; with every action in the process we simultaneously realise the possibility of the model of the outcome. For example: with every action we realise the agreement we have with the others sharing the same model (for one common goal there is one model recognised by all participants who execute possibly different activities with different goals to reach the common goal).
Based on a model, activities can be split up, organised and coordinated between agents. This usually is done to have the activities performed by the agents with the best fit. It also could be done because it is impossible for one agent alone to perform the activity. Doing so a chain of actions is formed. Some actions can be executed in parallel, others in series. An agent can start the next serial task in the chain only when the previous task has ended. This has to be understood as follows: it is only when an agent “takes over”, that the previous task ends. It is the responsibility of both agents to handle and communicate the model such that this is possible.
Activities with a goal are closed by the outcome.
We experience also activities not conforming to the picture already given. We experience that it is impossible to have a complete model of what we achieve when we want to achieve something. Simultaneously we always will achieve also something different, and this was not our goal. This outcome, that cannot be chosen, not available during the activity, not manageable, can enhance or can destroy the outcome that was wanted and chosen. Anyhow, the activities without goal do not end (in contrast with the activities with a goal), they change continuously, they make that we can observe endurance, they create the context for the activities with a goal. Typical activities with an important share of this character are creations, language, communication, learning, ...
Activities without a goal are open by the outcome (open and closed are used in the formal sense of a topology).
We will show now the model at work, starting with the most simple assumptions.
An agent “a” expects an activity to yield 1000.
“a” brings it to a favourable ending: “a” gains 1000.
Suppose that a needs a partner to arrive at a result: b
They agree on 2 tasks:
a spends 2 hours
b spends 3 hours
Hence: x/2 + x/3 = 1000
x = 1200
a gains 600
b gains 400
It has absolutely no sense to investigate where the yields are made. If a and b added value, but did not coordinate their actions (Heylighen 2011: they should align, divide labor (parallel action), perform the workflow (serial action) and aggregate the outcome), there was no final yield at all, no final value to give a perspective to the local values, nothing to quantify and nothing to share. They would have performed an activity without a goal. The perspective that is provided by the final result frequently is more than the sum of locally added values (at least because always something different happens also).
Suppose that they have to make costs to arrive at a favourable ending. Suppose 1000.
It has absolutely no sense to investigate where the costs are made. If they make only costs and do not collaborate, there will be no yield at all, and costs cannot be put in perspective. The perspective that is provided by the final result frequently is more than the sum of locally made costs.
Hence:
x/2 + x/3 = -1000
x = -1200
a gains -600 (looses 600)
b gains -400 (looses 400)
In this example profits and losses are equilibrated. But in all cases: when profits and losses show a certain ratio globally, they show exactly the same ratio locally.
The model at work shows the major influence of time: the time someone is spending in the process has more direct impact for someone spending little time on the process than for someone spending more time. This means in general that when people expect a global profit that they will try to spend as less time as possible personally compared to the time others spend. They will try to achieve the personal goal of the local activity taken up as soon as possible. When they expect a global loss, they will try to be associated with the process for a longer time. An activity without a goal does not fit yet in this image because we suppose that the common goal is reached and thus that the agents have something to share.
Suppose that b comes to the conclusion that he can perform very quickly some part of the task taken up (for example: he is skilled enough to make a certain choice without a lot of investigation) and that the other part could be done more quickly by agent c because c is more skilled to use some special tools (this is known as “farming-out”). We suppose that c agrees with the expected profitable outcome (perhaps it is a robot), so he takes over from b and ends the undertaking.
There are 3 tasks now
a 2 hours
b 1 hour
c 3 hours
Hence: x/2 + x/1 + x/3 = 1000
x = 545.4
a gains 272.7
b gains 545.4
c gains 181.8
The same applies for costs that can be shared.
The profit/loss ration is higher for b now, with a higher ratio b has more profit, with a lower ratio b has more costs. This means: he only is incited to collaborate with others when he is convinced that his local decision is one that globally reduces costs or increases yields.
Suppose that b realises that he can do very quickly some part of the task taken up, and that the other part could be done far more quickly by an other c. We suppose that this c agrees with the expected profitable outcome, so he takes over from b and ends the undertaking.
a 2 hours
b 1 hours
c 2 hours
The total time decreases now from 6 to 5 hours.
Hence: x/2 + x/1 + x/2 = 1000
x = 500
a gains 250
b gains 500
c gains 250
As expected, the share of b decreases, but the overall time, the time with which one enters in competition with other systems also decreases. This could mean: the group (a, b, c) has a profitable result to share, a competing group has no result, and nothing to share. As already mentioned: we want to stay as close as possible to reality: competition between groups and species is (and always has been) a major ground for trying to reduce the time that an activity takes. Competition means that there is no common or shared goal between the competing agents (groups) and that the agents (groups) are engaged in a zero sum interaction.
In a competing environment, when actions are rewarded inversely proportional with the time spend, there is a tendency for teamwork. When actions are rewarded directly proportional with the time spend, there is a tendency to separate the agents, the fittest only counts, there is no stimulus to work on communication skills and a pressure only from outside can force agents to increase their (inter)action speed.
Let us investigate now how the (a, b, c) team could further reduce the globally spent time.
This could be done by carefully selecting the best fit and changing the rules in the team or the team itself. Suppose that b knows that an other c is working more efficiently, but that b needs to spend more time in the communication to bring c to that point. The total time decreases further, the b time increases however.
In figures:
a 2 hours
b 1.5 hours
c 0.5 hours
Total time 4 hours only
Hence: x/2 + x/1.5 + x/0.5 = 1000
x = 315.7
a gains 157.85
b gains 210.47
c gains 631.4
Suppose that b did not care about the communication needs of that c, and decreased the time he spent on his task hoping that this would give him a higher profit. We suppose that this c agrees with the expected profitable outcome and what he gets from b, so he takes over from b.
a 2 hours
b 0.5 hours
c ... hours
This c however never comes to an end for the undertaking. Apparently the team spirit risks to get broken and the whole project is jeopardised. When there is no global and quantified result that can be shared, there is no local profit or loss and what the (a,b,c) group performed (an activity without a goal) is something completely different than the process they had in mind. The final outcome is in danger if nobody reacts. Because the three agents will share the same result (if any), they are motivated to see what happens globally and they could take the measures locally to come to an end. They do not act as individuals but as a team.
Suppose that b understands he is the agent who quickly can put some effective time in the process.
We distinguish now 4 tasks
a 2 hours
b 0.5 hours
b 1 hour
c 0.5 hours
A total time of 4 hours
Hence: x/2 + x/1.5 + x/0.5 = 1000
x = 315.7
a gains 157.85
b gains 210.47
c gains 631.4
Coming to a deadlock is not sanctioned automatically at the point where the process ends. Coming to a deadlock triggers the level of the internal communication.
In real life it is not always clear if the communication will succeed. A transparent structure of interaction therefore is more favourable. In real life we easily can observe that it is better that the agents in the group share a common culture. Highly performing systems will work in a very efficient communication culture and they will handle “farming-out” more effectively and efficiently. The time spent in the building up of a common culture will not end, it is not an activity with a goal, it is lasting and frequently more energy has to be put in. The result will be that the time spent for activities in the team with a goal drastically will be reduced. Teams are highly structured organisations, they are competing indirectly with each other in their use of activities without a goal for the benefit of the activities where they compete directly for a certain share. The benefits and costs of activities without a goal are not shared after the realisation of the model, but are shared in the activity itself but are valuable only when there is an outcome to share.
Moreover, there is an other possibility to reduce the time it takes to bring the process to a favourable end.
Suppose that more tasks are performed, take 6.
a 2 hours
b 0.5 hours
c 0.25 hours
d 0.5 hours
e 0.25 hours
f 0.25 hours
The total time is 3 hours 45 minutes
Hence:
x/2 + x/0.5 + x/0.25 + x/0.5 + x/0.25 + x/0.25 = 1000
x = 60.6
a gains 30.3
b gains 121.2
c gains 242.4
d gains 121.2
e gains 242.4
f gains 242.4
When some tasks can be executed in parallel, only the tasks situated on the serial path taking the most time (the critical path) will determine the time of the whole process. Hence, as long as a task is not on the critical path there is a slack related to it. We can understand now that such a task is ended only when the following task on the critical path starts, although the task could have ended earlier in reality (recall our definition of an ending task).
Suppose that three tasks (b; c + d; e) run in parallel as one block between a and f.
The longest path is a + c + d + f and takes 3 hours.
Because b (0,5 hours) and e (0,25 hours) work in parallel with c + d, the time spent can only be the sum of times by c and d: 0,75 hours
Hence:
x/2 + x/0.75 + x/0.25 + x/0.5 + x/0.75 + x/0.25 = 1000
x = 75,9
a gains 37,9
b gains 101,2
c gains 303,6
d gains 151,8
e gains 101,2
f gains 303,6
A consequence is that the tasks on the critical path will attract all attention, conformable to their influence on the total process time. Agents b and e are highly motivated to follow the activities of c and d. People also only will try to perform tasks in parallel when possible, if it is needed to decrease the whole time the process takes. They will have to balance the global results with their own profits. They will have to understand the global context (the competition), so they will have to spend time on this understanding. We can easily understand that the time related to the slack (0,25 hours for b and 0,5 hours for e) is spent by enhancing the communication in the group or more individually as free time. These last activities are activities deliberated from any direct goal with a measurable and divisible result, because there is no common model. These activities will structure the group or nurture individual agents and probably make the team more successful compared to other groups: they show a higher collective intelligence, because they thrive on diversity (more different talents), independence (everyone contributes for also its own profit), decentralisation (more actions run simultaneously) and aggregation (all have the same model of the outcome). These are the requirements for wisdom of crowds (Heylighen 2011 referring Surowiecki 2005).
It is now easy to extend what was understood when activities are executed in parallel in a team to a more global system. Because we also can envisage now that with a competition between teams (all trying to realise simultaneously the model of the outcome) the whole system of competing teams “tries to find the quickest way”. The whole system behaves like an individual agent and thus could be given an “individuality”. Put differently: in a competing environment with identical models of the outcome of activities, the quickest road is the road with the highest organisation. Teams with the highest organisation are best fit. The time spend during the slack of the process clearly shows the influence that the reduction of spent time has on activities: one has to introduce a more complex structure in the process elements. Structure is space-like as we will show in the next paragraph.
Still the time spent can be reduced further. When performing a task it is possible to perform an other task simultaneously. This is something different from “running in parallel”. When I perform two tasks simultaneously, than, with an action on the first task, I also perform an action on the second task, there is no need to take a second separate decision.
Because this can sound too abstract, we give first some physical “space-like” examples: when I move a bottle, I move simultaneously the liquid in it (what I wanted). I not only move simultaneously the liquid however, but also the air in and around it. This is context-dependent and relevant only in some environments (“normally” it will not bother me, neither the movements of the liquid inside the bottle, but in some environments the air moving around the bottle will perturb dust for example or toxic substances).
When I drill a hole in a plate for a shaft, I simultaneously change the free surface of the plate and I increase its temperature (what could give me problems in some applications). These ‘side-effects’ do not ‘consume’ more time. Matter seems to be organised in space. With every change (ordering in time) simultaneously a lot of other events happens and the relevance can only be judged when a context is included. We can understand that, with an other level of organisation, not only other events happen but also some same events. For example: when one understands a bicycle pump as simultaneously realising a pump, the effect of warming up the air in the bicycle pump by compression can also be understood as happening in a fan, also realising simultaneously an air-pump. An agent who “understands” this kind of simultaneity will be able to quicker take some decisions.
Thus we define: two points are experienced simultaneously when, with my choice for one of them, also the other is chosen. One decision introduces other points. This means that I do not have a freedom of choice between the points any more. If we agree that I always have a certain freedom of choice, than it follows that two points are experienced simultaneously when I have (experience) the freedom of choice between one of the points and something different from the other point. It is exactly because I have not a specific freedom of choice that there is no time spent. This limitation on a certain freedom of choices is the structure that agents carry with them. It is exactly because a certain structure is available that there is no time spent.
Usually simultaneities are self evident and are lived unconsciously (and this is what we call “unconscious”). This is typical for winning teams: with every action the “team-spirit” is lived simultaneously, a whole context is taken into account unconsciously by every member. This gradually became evident during the 70's when the industry became aware that the prevailing information technology failed to capture essential aspects of the cooperation in teams and eventually led to the insight that tacit knowledge is a distinctive organisational asset (Baumard 1999). People are not necessarily aware of their tacit knowledge, do not need to be aware to use it, and are not inclined to make efforts to become aware of it and to be able to share their knowledge if they want to protect their competitive advantage in the organisation. During the 70's the technology was not available to handle explicit knowledge. Today technologies are being developed to extract tacit knowledge from actual actions, actual practice. With the presently available information retrieval systems linked to recommendation systems a decisive step in that direction is made (Stenmark 2001, Widdows 2010). Autonomous software agents could anticipate interesting aspects for other agents based on the agents actions, thus the autonomous software agent would show some intelligence that can be extracted from actions. The given examples show what kind of formalisation is needed to extract simultaneous concepts from the context: we can make these simultaneities more “consciously available” by including selected context. This does not mean however that the autonomous software agent will externalise the knowledge from the interacting agent. This does not mean that the agent is rendered redundant as he might fear, because he still is the source of action.
The given examples show even how to design new simultaneities with the purpose to achieve something new (for example to reduce the time it takes to carry an activity to a desired end), and this is a new approach for research by design, by action.
The preconditions for the development of a new paradigm for time as explained is this paper are introduced in other papers in a scientific and formal setting. There I model the already proposed definition of simultaneity in a very formal and operational way. I argue that there is no need to make a difference between the simultaneity of “intrinsically correlated” points (when one point is a subcategory of another point) and the situation where one point causes another point. If “cause” implies “time”, I believe that time can be designed indeed. In this field my activities in the last 40 years as designer, engineer and scientist are situated.
We have seen that in a competing environment with identical models of the outcome of activities, the quickest road to a profitable outcome is the road with the highest organisation. Teams with the highest organisation are best fit. This means that we are looking at an organisational level: not the individuals are best fit, but a collective of individuals including their tools, instruments, procedures, environments etc... with the highest organisation given the local constraints. “The highest organisation” does not mean that the organisation does not need to change as environments change. This was discussed succinctly in the examples.
That the best organisations of agents are best fit is not apparent in the industrial cooperation of people alone. Not only in the industry this results in a noticeable increase in time pressure. It can be observed with the distribution of information or knowledge, an asset that in principle should be non-rivalry because it can be distributed without loosing it. Except... when it is related to reputation, when the knowledge creator, for being part of a team that distributed the information more quickly than competing teams, can claim (s)he was the first and is rewarded for this. This is a reward that is exclusive by the exploitation of linear time and is related to the activity of publication of the information. Thus it is an activity with a goal but in its organisational aspects benefiting from the “context building” activities without a goal. These context building activities increase the collective competence and act as the necessary medium for publication. This medium is very sensitive to disturbing actions from “free riders” and thus functions as a new kind of commons.
Baumard Ph. “Tacit Knowledge in Organisations” Sage Publications London 1999.
Heylighen F. and Vidal 2008: “Getting Things Done: The Science behind Stress-Free Productivity” Long Range Planning Volume 41, Issue 6, December 2008, Pages 585-605 available: http://pespmc1.vub.ac.be/papers/GTD-cognition.pdf
Heylighen F. 2011: Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence
Stenmark D. “Leveraging Tacit Organisational Knowledge” Journal of Management Information Systems, Vol 17, No. 3, Winter 2000-2001, pp. 9-24 available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.1636&rep=rep1&type=pdf
Widdows D. “The Semantic Vectors Package: New Algorithms and Public Tools for Distributional Semantics” Semantic Computing, 2010 IEEE Fourth International Conference