The 34: International Conference of the
System Dynamics Society
Delft, Netherlands
July 17 — 21, 2016
Modelling the Digital Camera Use
(The “Opportunity-benefits” paradox)
Tlir Rodiqi
University of Prishtina
Johann W. Goethe 2, Arberi 10 000 Pristina, Kosovo
+ 377 44 120 790
ilir.rodiqi@roze-ks.com
Abstract
Digital cameras have made revolution in photography by enabling enormous opportunities for
the photographer. However, for an ordinary person, most of the pictures shot with digital
camera cannot be fully utilized because they remain hidden into computer archives for a long
time without being developed. This creates a paradox between the opportunities and the
achieved purpose. This study develops a System Dynamics model of digital camera to present
h where ex} ially growing opportunities are not necessarily proportional to the
benefits achieved. Model analysis results describe existence of the paradox due to a growing
pattern during the picture-taking and passivity during the deletion of unnecessary pictures. This
lack of activity in balancing the system creates a paradoxical situation and a growing
accumulation of photos into computer archives.
Key words: System Dynamics, small behavioural systems, paradox, modelling.
Introduction
The photo camera was developed through intensive innovation of new technologies. The latest
versions, digital ones, enable wide range of benefits to the user: more photos, instant visibility,
small size, independence from the developer, different effects, change of picture quality, etc.
However, from everyday experiences in using the digital camera, it can be realized that produced
pictures are constantly accumulating into a computer with little chance of being utilized. This
kind of situation shows a paradox of disproportion between the abundant opportunities created
through innovation and the final benefits generated by camera use. The paradox problem is
widely investigated within a range of subjects related to management theory, such as: economy
(Clark, 2008), psychology, marketing (Jules & Good, 2014), leadership (Smith & Lewis, 2011),
technology (Norman, 2013), etc. Norman (2013, p.34.) identifies technology paradox as pressure
on the user created from requirements to use technology: “The same technology that simplifies
life by providing more functions in each device also complicates life by making the device harder
to learn, harder to use.”
Modelling the system of digital camera is primarily aimed at developing a small model of
behavioural nature that can be used for discussion of different similar paradoxical situations.
Small models are characterized with a limited number of core variables in a system. Such
situations can be considered closed systems because they present unique phenomena that can be
independently analyzed. They are usually systems that describe a pattern of behaviour and that
can also be considered similar with other situations. Examples of small systems are: Bass model
(Bass, 1969), Lotka — Volterra (LV) model (Groesser, 2012), TAM model (Davis, 1985), etc.
One important issue related to these models is the subject of analysis. The model can take into
consideration a particular population or individual. The digital camera model aims to describe
the behaviour of the individual and not generalize behaviour of the population like in the cases of
Bass and Lotka-Voltera models. Furthermore, such a model is not developed to describe solely
technical flows of the pictures but also to describe a system of behaviour that enables the use of
camera and human policies that rely in the background. These types of small models that
describe behaviour should be considered a specific modelling field and might be identified as
models of Small Behavioural Systems (SBS).
The developed Digital Camera Model presented in this paper is not considered a completed task.
The parameters for the model are supposed by the author based on personal experiences and
further measurements should be undertaken to define precise values. Ideas for the next stage of
developing the Digital Camera Model are to continue with more precise quantitative analysis and
model details, with validation of the model and development of an interactive simulation device.
Dynamic hypothesis of Digital Camera Model
Through the description of the model of digital camera use, the core intention of this study is to
analyze the paradox between opportunities and benefits. If the new technology brings more
opportunities it could lead to the increase of self-indulgence which then dominates over the
growing pattern and causes unintended consequences. With uncontrolled growth, there is risk
from overload, time constraints, and deformation of the system itself. This situation therefore
requires adequate balancing policies to reduce the possibility of creating extremities. General
pattern of the behaviour related to use of digital camera follows situations where different
opportunities are utilized by the particular subject. This “Used opportunity” life pattern is
basically “Limit to grow” archetype because the opprotunities has growing character and the
necessity to control the accumulation creates a balancing loop (figure 1.)
Opportunity Depletion
+ =e +
+p -
Opportunities t+) Accumulation C} Selection Purification
+ + %
2 Benefit
Self-indulgence Proper Result
Waste
Figure 1. “Used opportunity” life pattern
However, in this case the balancing of the accumulation is represented by two feedbacks. One
balancing feedback selects the proper results and utilizes them as benefits while the second
balancing loop accumulates waste and purifies the system. If this pattern is described through the
digital camera use, then the Digital Camera Model has three subsystems: ‘Picture-taking’,
‘Download’, and ‘Select’ (figure 2.). While the photos are taken in several sequences that are
randomly distributed in time, during the picture taking process, the camera memory is
accumulated. When the camera is filled with photos, they are downloaded into a computer
archive. If the user does not select the photos immediately, then he/she decides after a delay to
deal with the photos in the archive by selecting which photos are appropriate for further
processing. Selected photos can be distributed further for hard copy development to make the
selected photos usable.
Serie 1
veg sie
‘DELAY in : Download ; DELAYin Pures beveioe
taking new Memorize 1, pictures in Archive | selecting dave S ciwes
pictures Photos in |” Computer | Picturesin] pictures develop p
Picture- ——m—e— | Camera computer } ————— in hard
taking copy
So
Serie n
Figure 2. Camera use sequences
Utilize
hard
copy
pictures
(Ultimate benefit)
Casual loop diagram for the camera use is shown below (figure 3.). Picture-taking and
downloading the photos are presented within the same positive loop. Selection of the pictures
and their deletion are presented by two other loops. First of these loops is “Memory purification”
loop that enables the user to clear the archive from unsuccessful photos. The second of these
loops is called stimulation loop that enables the user to select the best pictures, utilizes them and
thus create a personal stimulus to make more pictures. This loop is different from the general
pattern presented in the figure 1 because development of the digital photos not necessarily means
that these photos will be deleted from the memory. The user might leave the digital copy into an
archive.
Archiving
Delay
Picture Decled
. Takin epletion “>
+y- Purification
Digital + Picture C} i _
Camera Archive Purification
Opportunities Failed Delay
a + n ‘icture
Picture Taking Picture
Stimulus +) Results
+ ain
Developing
Pe Delay
Benefit. + Pictures J “ey
==
Develop
Figure 3. Casual loop diagram of camera use
The first “Picture-taking” subsystem is characterized with two elements: the Picture-taking
process, the state of photos in the camera, and the downloading of the pictures into a computer
archive. Picture-taking depends on two factors. The first is availability of space in the camera
memory. The second is the picture-taking strategy. There are three basic options to deal with this
sub-system:
a. In the simplest option, the user takes pictures until the camera is completely full and then
downloads them, while by downloading the pictures they are also deleted from the
camera memory;
b. The second option is that after each series of shooting, the user downloads the camera
photos that have been taken. After that, since the camera memory is not emptied, the
photographer can choose to delete the downloaded photos or leave them in the camera;
c. The third option is when the user randomly downloads the photos after more shooting
series or when the camera is full.
This model considers only the first option that the photographer is using the camera until the
memory is full and then downloads all photos into a computer while deleting the photos from the
camera memory.
The second variable that influences the shooting rate is the activity related to camera use or
“Picture-taking strategy”. This variable is not very easily predicted but has some specific
characteristics. The use of camera is done on the occasional basis by undertaking sets of photos
through a certain period of time. For instance, the user is on travel and takes a lot of photos. This
means that the camera is used for a certain period of time (during the travel) and a certain
amount of photos is taken. However, there is also another way of using the camera when the
timing between two sets of shootings is very short because the camera is used very often. This
can be a case of the professional photographer or a photograph shop. In this case, the shooting
variable can be predicted as the average daily shooting rate. In this phase of the model, two
options for the shooting strategy variable will be considered:
1. The flat rate of the specific number of photos per day, and
2. The random impulse functions of several impulses during the time horizon period.
It is also considered that time available for shooting is reserved by the photographer
independently from his/her overall available time and therefore, in this study, the time used to
take photos will not have any influence on the shooting strategy. Otherwise, it is necessary to
connect the shooting strategy with the photographer available time.
The “downloading” sub-system starts after the camera memory is full. In one day, the
downloading empties the camera and fills the computer archive with photos.
Through the “Selection” sub-system, the computer archive is reduced from time to time when the
user deletes unsuccessful pictures. How the user deletes the photos depends on the implemented
deletion strategy that can have two options:
1. Immediately after downloading the photos, the user checks the successful and
unsuccessful ones and deletes the failed pictures;
2. The user accumulates all pictures after the download and delays the deletion of particular
photos for some other time while the selection process has been undertaken.
Both of these options are possible and feasible and should be considered separately during the
quantitative modelling. Moreover, the selection subsystem enables the user to reconsider the
pictures that are accumulated into a computer and select the particular ones to be further
developed as hard copies and framed or placed in albums. Development of the pictures as hard
copies can be considered a final user benefit achieved from the digital camera.
Stock and Flow model
Model boundaries are defined by ignoring issues such as the technology innovation cycle,
opportunities to intervene in the quality of picture, and the available time of the user.
Endogenous variables are related to the activity of the person to manipulate the camera. At this
stage, Digital Camera Model covers basic variables that enable camera use. These activities are
presented in more detail for each particular sub-system in the next chapters.
The overall stock and flow model of the digital camera is illustrated in the figure 4. The
timeframe of this model has been set to 1,200 days. The basic model has two stocks: the camera
photo archive and the archive of photos in the computer. These two stocks are changing based on
the picture-taking, the download and the deletion rates. Other auxiliary variables are enabling
functionality of the camera use according to the logic of the system. Each camera has its own
maximal capacity on the memory and this memory is depleted by the camera use until the
moment the memory is full. In this model, maximal capacity is set to 4,000 pictures. When the
memory in the camera is full, the downloading starts and at this moment the ‘Picture-Taking
Switch’ blocks the ‘Picture-Taking Rate’ because of the assumption that when the camera is
downloading it is not used to take pictures.
Maximum Camera
Capacity
ies
capac 2
Camera | | Photo | ~_
chive rag
os Picture Archive _| Download | Archive: |
Deleted photos
a taking rate
+ rate’ ' /
Picture mA i
Picture Taki Downloading Time :
Taking \ Factor fraction Deleting
Scenario fraction
Figure 4. Basic model of digital camera use
The picture-taking depends on the scenario that has been decided by the user. In this basic option
of the model, the ‘picture taking scenario’ is considered a flat value of 100 pictures per day.
Downloading is a very fast activity that is usually performed within few hours and therefore it is
estimated equal to the camera memory value per day. Deletion of the photos in this stage of
modelling is also considered a constant value. It is estimated that 250 photos will be deleted
within a week of 5 working days.
Results of the simulation show that flat rates of picture-taking are constantly utilizing camera,
which is then downloaded into a computer after its capacity has been depleted. Due to the
constant flow of pictures into a computer, the archive of pictures in the computer increases
linearly (Figure 6). Because of the constant deletion of pictures, the linear increase of pictures in
the computer archive is being constantly reduced for a certain amount during the deletion (Figure
6.). However, since the inflow rate into a computer archive is higher than the outflow rate from
deleting the photos, the archive is growing fast. The diagrams in the figure 5 show that the
archive is empty until the first photos are downloaded and there is also no deletion of the photos
while the archive in computer is zero.
Camera Archive
4,000
3,000
2,000
1,000
0
Download rate
4,000
3,000
2,000
1,000
0
Picture taking rate
100
75
0 300 600 900 1200
Figure 5.
Photo Archive
60,000
45,000
30,000
15,000
0
Deleted photos
60
45
Figure 6.
Flat rates for picture-taking and deletion do not present the realistic behaviour of the system.
Picture-taking is usually not a flat rate but series of random pulses with different height and
different timing. The user takes pictures at different moments in time in the length of a time
interval. The deleting rate is also more complex than the constant option. Figure 7 shows another
level of details identified in the DC model. These additional variables are mostly related to the
intention to define key stocks and flows in a more comprehensive manner.
Maximum
Camera
Capacity
Ayailabl
Camera
Capacity
Camera
Computer
Archive 2 Photo
Planned Picture Download Archive | Depted
l aking ti
rate + Rate” aa NE photos
4
Picture Poors Downloading Deleting
cenaric Switch Factor + action
™ Scenario “ ae
A Picture Selection Faas
View Viewing habits action
we gE
Picture Taking NE Scksion
C) volume
mene Selection
factor
Blancing "+ Level in
factor <———— ‘Archive
AN
Reducing Expanding
policy policy
Figure 7. Overall model of the digital camera
Picture Taking Rate
2,000
1,500
1,000
500 | |
Wt
0
0 120 240 360 480 600 720 840 960
1080 1200
Time (Day)
Picture Taking Rate :
Photos/Day
Figure 8.
To define a Picture-Taking Rate, a new picture-taking scenario was assumed. The basis of this
scenario is a pulse function with random frequencies (figure 8). This type of function shows the
initial behaviour in the form of occasional activities of the camera user. Under this regime of
camera use, the stock of photos in the camera changes frequently with filling and emptying the
camera memory (figure 8).
Camera Archive
6,000
4,500
3,000
1,500
0
0 120° 240 360 480 600 720 840 960 1080 1200
Time (Day)
Camera Archive : Ba Photos
Figure 9.
The scenario for taking pictures is also influenced by two loops related to individual policies of
the user. The first loop is the short-term satisfaction with camera use as a result of the possibility
to see the taken picture on the camera display. When a picture is taken, the user looks at it on the
display and usually decides to take another photo if the actual one is not satisfactory or makes a
new photo with a new outlook. The second loop is related to the activity of the user to control the
amount of pictures in the computer archive. This is regulated though the balancing policy of the
user. Based on the desired level of the computer photo archive, the user can choose to either
reduce or expand the camera activity.
Selection habits
0.5
0.25
0 120 240 360 «480 «600-720-840 «960 1080 1200
Time (Day)
Selection habits Photos
Figure 10.
Finally, additional variables are attached to the cycle of deleting photos. Similar to the picture-
taking scenario, it has been assumed that the user will tackle the computer photo archive from
time to time. Again, a pulse repetition variable has been set (figure 9) which then creates a basis
for the deleting rate. Influenced by the downloading and deleting rates, the computer storage
changes accordingly (figure 10).
Enhancing stock and flow relationship understanding through
experiential learning
Jorge Ramon D’ Acosta Rivera, Edmilson Alves de Moraes
Centro Universitario FEIT
ABSTRACT
The stock-flow failure, cognitive bias that distorts the judgment of the relationship
between a stock and its related flows, has implications for many decision-making
processes in organizations management. Therefore, it is a relevant issue in new manager’s
education. Although many studies have replicated the stock-flow failure, few have
provided insight into effective interventions to improve the performance of subjects and
none of them presented a model derived from a theory of learning to improve the poor
performance understanding. This study aimed to test whether the experiential learning
improves students’ performance in the accumulation problem. Based on the theory of
experiential learning and the detailed analysis of the problem, it was developed an
intervention, in the form of a problem-based learning PBL, that promoted the operative
exercise of some necessary coordination for the solution of the stock-flow problem,
improving the performance of students undergoing this intervention. This improvement
was not observed in students undergoing a placebo intervention.
Keywords: Systems thinking. Stock-flow failure. Management education. Experiential
learning
Computer Photo Archive
40,000
30,000
20,000
10,000
0
0 120 240 360 480 600 720 840 960 1080 1200
Time (Day)
Computer Photo Archive : Bas Photos
Figure 11.
Discussion and conclusions
The above described model presents an initial effort to describe the dynamics of the system to
use the digital camera. The actual model shows that if the camera is constantly utilized, the
computer archive grows rapidly. This growing loop is reduced only by deleting the photos or by
setting up policies to reduce-picture taking levels. If the pictures are selected and deleted with a
slower pace than the picture-taking, then there is overload of the computer’s archive. This
overload is a sign of the paradox that the system lacks a balanced use.
This level of quantification of the system is not intended to be comprehensive enough because
the model requires further details and analysis. In the further stages, the model requires
interventions in:
1. Additional modelling features about the system;
2. Consideration of the new factors that influence the behaviour (see figure 12); and
3. Further experimentation with the model to identify characteristic behaviour.
Further modelling of the system requires more precise quantitative analysis, definition of the
parameters, definition of the exogenous variables, calibrating and validation of the model. New
factors that should be taken into account are available time of the user, additional engagements
of the user, increased technological innovations, and social networks impact.
Finally, experimenting with the model could bring important answers about the behaviour of the
camera user but also about the behaviour of different systems that perform under similar
circumstances, like: the level of personal satisfaction in case of saturation with personal
activities, addiction towards the use of social networks, situations of quality improvements that
are not increasing usefulness of the product, behaviour that creates obesity, or our behaviour
towards the environment when we increase our investments without control and destroy the
nature.
Picture taking. Capacity of the
habits memory
Picture a Archiving Purification
Devices Delay Strategy
oS Taking. Depletion“?
a Purification
Digital Picture S 4 ”
Camera Archive Purification
+ Opportunities 4 Dela:
a Failed y
_ n Picture
Innovations Picture Taking Picture
Stimulus Results \—
Time
available
Social Networks Developing
Impact + Delay -
Pictures to“
Develop
“Benefit +
Other personal
activities
Figure 12. Different additional factors that might influence behaviour
In general, there are three conclusions from this study which relate to the basic principles that
describe the behaviour of the situation where opportunity paradoxical situation occurs:
1. The Digital Camera model shows situation of accumulating large amounts of results
which are not necessarily utilized in the same proportion, thus, describes the paradox that
is created from abundant life opportunities that create limited or sometimes distorted
benefits;
2. Digital Camera Model describes
situation based on a “Limits-to-grow “archetype and
belongs to the category of “Small Behavioral Systems (SBS)” which describe simple
behavioral patterns that could be used to explain other similar living phenomena.
3. Extension of the model by taking in account other constraints, shows domination of
positive loops and necessity to undertake more balancing measures to create harmonized
conditions.
I would like to acknowledge the reviewers of this paper for their constructive and helpful
suggestions! Also I am thankful to the participants of the System Dynamics Conference in Delft
who showed their interest for the idea to model digital camera use. The discussions with them
were important inspiration for my future work.
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To generate startup growth from the viral engine, the entrepreneur intends, through contacts
with investors, achieving what he calls the viral icons or potential customers who are
members of commercial organizations with several associates (other retails owners) and
admittedly have a strong influence on the other members. Those viral icons are represented
in the model by customers using the product. The other model elements are described as
following:
“Awareness of the product [auxiliary: product awareness] spreads rapidly from person to
person similarly to the way a virus becomes an epidemic...Instead, products that exhibit
viral growth depend on person-to-person transmission as a necessary consequence of
nomal product use...Growth happens automatically as a side effect of customers using the
product [auxiliary: customers using and spreading the product]...Like the other engines
of growth, viral engine is powered by a feedback loop that can be quantified. It is called the
viral loop, and its speed is determined by a single mathematical term called the viral
coefficient [auxiliary]. The higher this coefficient is, the faster the product will spread. The
viral coefficient measures how many new customers [CUSTOMERS inflow: new
customers] will use a product as a consequence of each new customer who signs up
[CUSTOMERS level].”
This description results in the figure 7.1. However, based on the entrepreneur mental
model, the structure is different without a feedback loop. In his view, the startup policy
should promote the attraction of viral icons, make them customers so they can attract and
influence others customers (followers). The figures of these models are shown below.
viral coefficient
total new
customers
new customers total customers — viral coefficient <¢——
product awareness
Viral Growth Cycle
product awareness
28 customers using and
spreading the product new viral icons
time for icons
prospecting
Figure 6.1 — Viral Growth Engine based on Ries Figure 6.2 - Viral Growth Engine based on entrepreneur mental
(2011) model
Thus, in entrepreneur model (figure 6.2), the viral coefficient, primary metric suggested in
Ries (2011), is based on the number of customers that brings new customers (calculated by
dividing the number of new customers by the amount of existing customers). However, the
figure does not represent any feedback loop, the contagion is directed performed by few
specific customers (viral icons), discarding the possibility of customers in stock participate
in the viral cycle.
This makes the engine inefficient due to a strategy that does not consider a viral effort or
product. Is worth noting that this gap occurs between the original definition of this growth
engine in literature, that considers the existence of the feedback loop, and the perception of
the entrepreneur, who does not conceptualize a growth engine based on a feedback loop
when set your viral strategy.
Figure 7 shows the viral coefficient behavior and its small impact on the acquisition of new
customers for the startup.
800
20
0 6 12 18 24 30 36 42 48 54 60
Time (Month)
total customers : Current
total new ¢ : Current
viral coefficient : Current
Figure 7: Viral Coefficient behavior based on Entrepreneur Mental Model
As aresult of misalignment between the mental model of the entrepreneur and the viral
growth engine proposed by Ries (2011), the viral coefficient reaches a brief initial peak
(month 4), when the viral icons begin to become customers, but then remains in a value far
below 1 until the end of the simulation compromising growth.
This behavior is the result of an increasing amount of followers customers in relation to the
relativity fixed amount of viral icons. Therefore, there is not a policy to promote product
virality for all customers but only for a specific and limited group. This can be explained by
the fact that this entrepreneur have not given much attention to this engine compared to
others
The table 1 below shows the gaps identified between the model originally proposed in
(Ries, 2011) and the one simulated as well as from the perception of the entrepreneur and
the simulation and original setting.
mental model AND
model behavior
growth even acquiring
many customers
(Reduce acquisition cost
and/or Increase
retention policy (Other
initiatives to retention
beyond R&D)
Gaps Identified Paid Engine Sticky Engine Viral Engine
Between Entrepreneur Startup policy ignores
mental model AND Ries reinforce feedback loop
(2011) (no policy to promote
product virality for all
customers but only fora
specific group)
Between Entrepreneur The startup do not Delays in R&D
AND model structure
estimated amount of
customers in the market
Customer outflow
absence
Customer Value) Constant acquisition of
new customers (paid
stick engine)
Between Ries (2011) Absence of total Absence of a reinforcing
feedback loop
Absence of an
acquisition policy for
new customers
Table 1: Identified Gaps in Research
6. Conclusion
With the results achieved in this study, we conclude that the structure and dynamic
simulation models of growth in information technology startups can be adopted to facilitate
more detailed analysis of concepts presented in the literature, particularly those of a
dynamic nature, collaborating with the development of the model mental entrepreneurs and
the concepts.
Currently, there are a lot of publications in the area of entrepreneurship on new concepts
such as lean startup, customer development, among others, and the large number of events,
competitions and organizations facing rapid and consistent growth of new business in
Brazil, including the support of the Federal government. Such initiatives occur in
misalignment with the research conducted in the area, which may contribute to the delay in
the development of theories in entrepreneurship.
In this regard, the proposed growth engines, presented in a book of quite insertion in the
entrepreneurial community of startups, The Lean Startup (Ries, 2011) helps to explain
dominant and dynamic logic that should be favored by entrepreneurs after minimally
validated their models business, but they need to make decisions and have more focused
metrics of growth. Through organic growth mechanisms proposed by the author, it is
expected that entrepreneurs do not lose focus during this process and know even when
changing engine, evaluating decisions and performance over time.
However, besides the gaps identified in this study, such engines proposed by the author
demonstrate a limitation related to the fact that give an emphasis on the development cycle
of market demands and do not favor internal aspects of the organization, such as capacity
building and investment feedback loops, as the market growth model originally developed
in and widely disseminated in publications such as in System Dynamics community
(Forrester, 1968; Morecroft, 2007; Sterman, 2000; Warren, 2002).
This study has some important limitations. One is that the analysis was not performed after
the startup's growth, although it occurred after its business model already tested a few times
in the market, to the point of even receive financial support from venture capitalists. It also
presents results based on the perception of an entrepreneur of a startup and was not
considered competition between companies.
As future research proposals, it is intended to identify these engines in action after the
growth of startups in order to understand how they present themselves, and to evaluate
whether there are other growth pattems not covered by the research. Such research can
promote a better understanding of the entrepreneur influencing their decisions.
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