Smoothing Away the Stagnation Problem of Community
Currencies with “Customized Communities” based on
Satisfaction Prediction by Neural Network
Maen Alaraj*, Makoto Nishibe***Good Money Lab, Senshu University, Kanagawa City, Japan, maen.alaraj@gck.co.jp**Good Money Lab, Senshu University, Kanagawa City, Japan, nishibe@isc.senshu-u.ac.jp

Abstract
Every community, no matter how money poor, has a wealth of abilities and capability to stimulate the local
economy. From this point, idea of the community currency (CC) was emerged and was proposed as a tool
to achieve a sustainable development in the local economy. However, creating a community currency was
not enough to energize the local economy without addressing the stagnation problem. Thus, in the current
research, we proposed a new framework (sequence of steps) to build the “customized community” where
the needs of members were met with the offered market to solve the stagnation problem. The theoreticalconcept of customized community was discussed in our previous research by utilizing random networkmodel while in the current study we used a real data recorded by CC-based platform called C.C.Wallet toestimate an satisfaction degree of the members of the community. Considering this, the backbone of the
proposed framework is estimating the satisfaction degree of the members of the community by utilizing a
Neural Network (NN) and this satisfaction degree was used as an index to determine the members who will
be given thereafter a “preference” in terms of bonus premium amount to be added to their initial purchaseof the CC with money. The proposed index was created based on the number of purchases of same goods
and service as well as by analysing C.C.Wallet users' messages (text-based comments and impressions)regarding the offered goods and services after completing the transactions in Japanese language. Thus, toanalyse the comment text recorded in C.C.Wallet, it is necessary to use the technology of Natural Language
Processing (NLP) where those comments were tokenized into tokens by using python language-based
module. In the current study, the engagement of the members with the provided market was monitored bycomputing a visual map of Shannon Entropy (SE). Our main findings suggest that proposed framework can
be considered as a tool to construct the concept of “customized community” where the circulation of CCs
can be accelerated and hence the local economy will be energized as a result.

Keywords
Customized Community, Community Currency Stagnation, Neural Network, Natural Language Processing.

1. Introduction
A money can be defined as a stand-alone information medium of exchange or a measure of value and theimportant condition of it is to be accepted by a group regardless of its size whether big or small groups(Kichiji, 2012). Considering this, shopping point, electronic money, mileage, exchange coupon andcommunity currency should be all called “money”.
In economics, community currency (CC) can be defined as a currency that organized and managed by alocal community, which involves exchange of goods and services. CCs can be spent within a particular
local geographical area for accomplishing the aim of revitalizing local economies. CCs have some features
like 1) relatively small or finite sphere of circulation, 2) issues by administrative committees, 3) nonconvertible or hard to convert into legal tender, 4) holding zero or minus interest rate. Since CCs have
limited circulation as mentioned earlier, and don’t be assumed to replace the national currency (the officiallyrecognized “money”), CCs are also known as complementary currencies (Tong, 2008).
Historically, communities have issued, managed, and circulated their own currencies for the past 6000
years (DeMeulenaere, 2000). The domination of national currencies and the global financial systems makes
the community currencies diminished in influence over time. However, financial crisis since 1980s havepromoted communities in Asia (e.g., Japan, Thailand), and the Americas (e.g., Mexico, Argentina) toexperiment with issuing local currencies to protect livelihood against the vicissitudes of the mainstream
economy (Tong, 2008). Since then, many types of CCs have been set up and proposed, but it seems thatthose CCs have also some drawbacks in terms of stagnation. For instance, “Eco-money” which was
proposed by Toshiharu Kato (Kato, 2001) as a special type of CC was used in volunteer activities among
citizens to activate mutual aids and stimulate social welfare services.
Eco-money was designed to be used in volunteer activities and social welfare services. Eco-money was
accumulated in the hands of participants (especially younger generations) who significantly contributed to
the volunteer activities. However, they could not find desired services and products in the market to spend
their “Eco-money” and hence stagnation occurred. The double triangle system (DTS) was proposed by
Makoto Nishibe (Nishibe, 2004) to cope with the stagnation issue. As shown in figure 1, DTS tried to makea bridge between non-commercial (volunteer related activities) and commercial transactions to stimulate
the participants to buy goods and services in the local markets. To prove the effectiveness of the DTS as a
CC system, the currency circulation of DTS was examined theoretically and empirically by applying it on
a community whose members were selected from Tomamae-cho city located in the prefecture of Hokkaido

in Japan (Kichiji, 2006, Kichiji, 2008). The effectiveness of DTS was determined based on the fact that thevolume of commercial and non-commercial transactions increased. However, some Tomamae-cho CC was
accumulated at specific business partners and could not circulate smoothly due to the limited market(Kichiji, 2008).

STS: Sigle Triangle System
Local
Residents

Local
Residents

The flow ofpointsMerchandiseTheaccumulated
points can beconvertedwith CC（Volunteer activities,
Social welfare）
Local
Residents

Labor
Services

CC Operating
Body

Commerce
and Industry

Nonprofits
Organization

Grants(Optional)

Events andCommunity Activity

Local
Government

Figure 1 Double Triangle System (DTS)(Kichiji, N., 2006)

Flow of CC:
Flow of Goods/Services:

On the other hand, numerous reports have been compiled that shows the ability of CC to foster the social
sustainability and have been assessed by Arnaud (Arnaud, 2015). Arnaud showed that economic benefitsof CCs are limited due to their small scale. To understand this limitation, we need to give an overview about
LETS which stands for “Local Exchange Trading System”. LETS is one of the account type Community
Currencies for whoever wants to use it and was initiated in 1983 by Michael Linton in Comox Valley,
Vancouver Island, Canada (Kichiji, 2012).
Transactions using LETS are recorded in each participant’s account. Participants can buy and sell
products and services from each other with specific terms of price and quantities on a peer-to-peer basis.
LETS can only circulate within finite physical or virtual domains. If you have a positive deposit in youraccount, you will not gain any interest from your savings.
In contrast, if you have no money and you want to buy something, you still can buy it by going below
zero in your account by creating money units. The money in LETS can be created by individuals to buy
goods or services without any limit or with a certain upper limit according to the rules of each LETS andthis is the advantage of LETS. However, this is completely different from conventional money issued basedon the value of commodity as money or the authoritative power of governments as issuers.
By conventional money, the seller will accept credit from the buyer and hence, the buyer incurs a debt
to the seller. Considering this, the debt is generated on the side of the payer. When the central bank issues
central banknotes, it gives a certificate of indebtedness stating that I (the central bank) owe you (a recipient),
and this is called an “IOU”.
However, a buyer is not directly in debt to a seller in LETS. Rather, the buyer is thought to be in debt tothe community, composed of all the participants in the LETS. The buyer should have an ethicalresponsibility to repay the debt to the LETS community. In such systems as LETS, debts and credits do notbilaterally but multilaterally balance out. In other word, LETS do not adopt bilateral netting but multilateral
netting. Then we call this kind of money as in LETS, not an “IOU” but an “IOC”, which signifies “I owe
Community”. Considering this, the larger the community of LETS becomes in terms of the number ofparticipants, the more the degree of anonymity will become, and under such circumstances, it’s hard tomaintain trust among the participants of the community and the stagnation related issue may arise again.
Apparently, to accelerate the circulation of CCs among the participants of the community, stagnation
related problem needs to be addressed. Thus, in our previous study (Alaraj, 2020), we showed throughrandom network simulation that the concept of “customized community” can be used as a tool to solve thestagnation problem, but in the current study we will introduce a computational platform to build such
communities by estimating the degree of satisfaction of the participants using Neural Networks. Thus, themain purpose of this paper is to propose a computational framework (or sequence of steps) based on real
data to build the “customized community” where the needs of members are met with the offered market.
The framework was created by applying a neural network to estimate the satisfaction degree of the members
who will be given thereafter a “preference” in terms of bonus premium amount to add to their initialpurchase of the CC with money.
Entropy maps were then computed to monitor the engagement of the members with the provided market
to achieve our goal that related increase the circulation of CCs among the members of the community andhence, the stagnation related problems will be reduced as a result.
For the practical purpose of improving the sustainability of CC, we will introduce the theoreticalframework of the customized community, and we will talk also about random network model and Shannon
entropy in section 2.
Next, in section 3, we will not talk only about the proposed approach to build such customized
communities by estimating the degree of satisfaction of the participants through applying neural network,
but also, the methods of building neural models will be explained as well.

Section 4 analyzes the results and tries to discuss the proposed approach and clarifies the link between
estimating the satisfaction of the participant and customized community.
Finally, the conclusions and the possible extensions of the current work will be exposed in section 5.

2. Customized Community
2.1 The Proposed Approach to Build a Customized Community?
Currency stagnation occurs when the circulation of currency in specific areas become less than otherareas due to many factors such as small number of participants or dissatisfaction with the local market.
Start
Estimate the satisfaction degree of themembers
Construct the customized community

End
Figure 2 The proposed framework
Thus, to revitalize the local economy by using CCs, we don’t need only to increase the number of
persons who would like to join the community, but also, we need to increase the number of transactions
using CCs. Such increasing in terms of number of participants and number of transactions will assist us torevitalize the local economy as the circulation of CCs among the members of the community will be
increased.
Such increasing is important because it will reinforce the cooperation among those members of thecommunity as the earned CCs from performing non-commercial transactions will be “absorbed” by its
subsequent commercial transactions and prevent CCs from stagnating halfway through the circulation asshown in Figure 1. Thus, we need to construct a market where the demands matched the offered services
and products of the other members. For example, if the number of members who are raising children in thecommunity is few, child-related merchandises will not be likely purchased by CCs through the community,
as there is a mismatch between the demands and the needs inside the community. In such cases, the problem
of CC stagnation will be arisen and hence, we need a more appropriate market where the demands of themembers can be satisfied. To this end, in our previous research (Alaraj, 2020), we clarified throughsimulation that building a customized market where the demands of members matched the offered services
and products of the other members inside the community would accelerate the circulation of CCs. In thisregard, we need to give an overview about customized community.
A customized community is a type of community of interest (COI) based on the commonality ofmembers' preferences for various categories of goods and services. If we can identify these preferences ofthe members by estimating members' satisfaction regarding a particular category of goods or services, we
can build a customized community.
For better understanding the approach of the current research, we will first explain the concept of“customized community” and then we will discuss how to build such communities. However, in practicalcases, we need to reverse of the order of execution as shown in Figure 2.

2.2 What is the Customized Community
As stated above, to strengthen cooperation among members of a local community by using CC, it is betterto increase “commercial transactions” after “non-commercial transactions” such as exchanges and mutual
aid among members. The DTS suggests that if the members of the community use the CC acquired from
non-commercial transactions in commercial transactions, this will increase the number of “commercial
transactions”, and the process of “absorption” into the market will be accelerated, rather than remaining
in the hands of the members.
Here, if any business partners inside a particular community receive CCs and expect that such CCs will
be accepted by the other the other shops, even if the business they are doing is not for non-profit purposes,
the conditions to consider CCs as a money will be satisfied.
As a result of this situation, the newly created CCs through the execution of “non-commercial
transactions”, can be called a “currency of trust” issued by the community rather than a “currency of credit ”
which is issued by banks and such “currencies of trust” will affect on the “commercial transactions” andwill revitalize the local economy as a result.
However, on the contrary, if the number of “commercial transactions” is increased, it will be difficult
to distinguish them from ordinary market transactions because of the prominence of commercial activities
for the purpose of legal tender, and as a result, there is a strong risk that cooperative relationships based ontrust among members will not be formed or will be lost.
Therefore, by reshaping the CC-based market to meet the demands of the members as much as possible
and avoiding the problem of currency stagnation in the middle, the “commercial transactions” will be
increased.
Customized community can be constructed by giving specific members who has a frequent transaction akind of “preference” in the form of a bonus premium amount added to the purchased CCs with legal money(e.g., Yen, USD, Euro etc.) to be used with business partners inside the community. Such a kind of“preference” can be considered as a strong incentive not only for the people who are inside a community,
but also to induce other people from outside the community to join the community and contributesignificantly in it. Thus, in this regard, we would like to demonstrate that we used the term “customized
community” rather than “customized market” to highlight the importance of participants who have highparticipation rate in performing transactions within the community.
Customized community needs some parameters and rules to “filter out” who can join the community, andsuch rules can be determined based on information about members who frequently participated in thetransactions of the community. For example, if we can select members who frequently trade in baby
products, we can form a community customized with the “commonality” of “child-rearing”. This would
include not only fathers and mothers who are raising babies, but also grandparents, relatives, and otherblood relatives who are interested in their grandchildren and cousins. Also, “commonality” can be identified
in terms of the estimation of the satisfaction degree of the members based on two factors as shown below:
1.

The impressions of the member who wrote his/her impression in text form, after finishing thetransaction using a CCs based computational platform called C.C.Wallet.

2.

The number of purchases for a particular service or products by a member in the community andhence, some concerns will be arisen due to privacy-related issues and such issues can be addressedby setting up a privacy policy and term of services in advance for the community. However, setting
up such privacy policies cannot easily clear up the privacy problem without also providing valuable
benefits to potential members of the community to induce them to share some personal related
information (e.g., service, product category etc.).

Since that the build of customized community is performed after estimating the satisfaction degree ofthe member, we will talk briefly about the simulation of the customized community in advance and moredetails can be found in (Alaraj, 2020), while the approach to build this customized community will be
explained in the current research.

2.3 The Simulation of the Customized Community
The simulation of customized community was examined throughout a random network applied using
Python and the details of the development environment is shown in Table 1.

Table 1 The Development Environment of The Simulation
Application Name
Jupyter Notepad
PythonAnaconda
Gephi

Version
6.0.3
2.7.174.5.4
0.9.2

The efficiency of the principle of “customized community” was examined throughout a simulation

using a random network. This simulation was performed by assuming that the community size consisted of
100 members (nodes) and the transactions were done among them randomly by selecting a buyer node anda seller node through generating a asymmetric adjacency matrix (Adj), where each value in this matrix
represented the volume of transactions between each buyer and seller in the community.
𝑎!!
𝐴𝑑𝑗 = % ⋮
𝑎#!

⋯ 𝑎!"
⋱
⋮ * , 𝑖 = 1, … , 100 𝑎𝑛𝑑 𝑗 = 1, … , 100⋯ 𝑎#"𝑎#" = 0 𝑤ℎ𝑒𝑛 𝑖 = 𝑗(1)

The principle of “customized community” was founded by calculating the entropy map of thetransaction participation rate of all members of the community. Thus, it is needed to track the transaction
history of community members over a period of time, and this period was assumed 50 days as shown in
Figure 3.
Announce

0

𝑇#

𝑇$

Time
𝑇!

𝑇%

𝑇"

%%%%%#
𝐴𝑑𝚥

%%%%%
𝐴𝑑𝚥$

%%%%%!
𝐴𝑑𝚥

%%%%%%
𝐴𝑑𝚥

%%%%%"
𝐴𝑑𝚥

𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔#

50

100

𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔$

𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔!

150

𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔%

200

Figure 3 Schematic representation of the duration of CC simulation
Figure 4 represents the asymmetric adjacency matrix (Adj) where the rows represent the buyers(consumers), while the columns of Adj represent the sellers (providers). The flows of CCs from the buyer
to the seller was represented by using arrows in the directed graph, as shown in Figure 4.
G
E
D

F
A

B

C
(Directed Graph)250

𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔" days

(Adjacency Matrix)

Figure 4 Example of Asymmetric Adjacency Matrix
This matrix is “asymmetric” because member X is a buyer, and another member Y is a seller, and it does
not necessarily mean that the reverse is true: one member X is a seller and another member Y is a buyer.
For example, Figure 4 does not only show the case where A is the buyer and B is the seller (thecorresponding element in row A, column B is 1), but also when A is the seller and B is the buyer (thecorresponding elements in row B, column A is 1).
Also, Figure 4 does not only show the case where A is the buyer and E is the seller (the corresponding
elements in row A, column E is 1), but also when A is the seller and E is not the buyer (the correspondingelements in row E, column A is 0).
Considering this, the adjacency matrix (Adj) indicates that each member wants to purchase “willingness
to purchase” the product or service offered by another member and this does not necessary mean that thetransaction was realized as it depends on the balance of CC that the buyer (consumers) had.
Next, the entropy map, as we will see later, represents a prototype for building a customized community
based on the “commonality” of the offered products and services from the perspective of sellers and buyers.
2.3.1

The Assumptions of the Simulation

The execution of this simulation was based on the following nine assumptions. In the start of thesimulation, we assumed that there were100 members in a virtual community and the initial amount of CC
is 10000 CC per person. This means that the total money stock is 1 million CCs, as shown in figure 5. The
period of simulation was assumed to be lasted for 250 days divided into 5 periods （𝑇& 、n = 1,…,5）.
1.

It is assumed that the product initial price (PCom) is randomly generated within the range of
50 <PCom ≤ 𝐼𝑛𝑖& , ∀𝑛 = 1, … ,5, where Ini is the initial amount of CC. Products andservices provided by each sellers (providers) were not bartered and should be exchanged
with CCs. As Figure 3 shows, the amount of CC, which were not used in community
transactions for 50 days (𝑇 & = 50,∀𝑛 = 1,…,5), was considered as a stagnation.

2.

It was assumed that the provided products and services can be categorized, and the membersof the community who make bilateral transactions can buy and sell products and services
using CCs. Any transaction can be realized if the member has an amount of CCs more thanthe price of the product or services (PCom) that they want to buy. If this condition is satisfied,
the price of product or service will be deduced from the CC which is hold by the member.

3.

It was assumed that the transaction is executed randomly. That is, a pair of buyer and seller
was randomly selected using asymmetric adjacency matrix. The Shannon entropy (SEn) was
computed after a certain period (50 days).

4.

The buyer executes the transaction using the CC given at the initial stage. However, there
will be some members in the market do not use CC and hold some or all of it. If theremaining amount of CCs was not used until the end of the period (50-day), this amount of
CC was considered as a “stagnation”. Considering this, when the participation rate (PR)decreases, the flow of CCs among the members of the community will be decreases as aresult and hence, stagnation state will be occurred. In the current study, the “participation
rate” was defined as a “willingness to purchase” which was represented by the “buyer-toseller” arrow in the network, where the buyer who was represented in the rows of theadjacency matrix, wants the seller's product or service which was represented in the column.

The realization of such ”willingness” was based on CC balance that the buyer has, and if
this balance exceeds the price of product/service, the transaction will be realized. Thus, fromthis perspective, the term “participation rate” rather than “transaction rate” was used in thecurrent research.
5.

As shown in Figure 3, it was assumed that CC-based market was opened for 250 days. The
amount of CC stagnation in each group and the total amount of CC retention in all groups
were calculated every 50 days 𝑇& ). Therefore, the stagnation amount every 50 days ((𝑇 & =50, ∀𝑛 = 1,…,5) was defined by ( 𝑆𝑡𝑎𝑔' , ∀ｍ = 1, … , 6 ), and the total amount of
stagnation was defined by (𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔& , ∀𝑛 = 1,…, 5), as shown in Figure 3.

6.

As shown in Table 2, the participation rate in each group (Group A, B, C, C, E and NS) was
described in Table 2 and was assumed to be constant throughout the whole simulation.

Table 2 The Groups of the Simulation
Stagnation GroupIndex of Group (m)Symbol of Group Name
Participation Rate (PR) (%)
Number of persons (Nr)
7.

1
A
90
10

2
B
80
10

3
C
70
10

4
D
60
10

5
E
50
10

Non-Stagnation
Group
6
NS
100

50

Also, the number of people in each group and the participation rate (PR) of the entire groupwere assumed to be constant throughout the whole simulation, as shown in Table 2.

8.

As shown in Figure 4, the transaction was performed throughout the following steps.
a.

Set the adjacency matrix.

b.

The commodity price was randomly generated in the range of PCom, 50 <PCom ≤𝐼𝑛𝑖& , ∀𝑛 = 1, … ,5.

c.

Randomly select a pair of members to be sellers and buyers. For example, select A
and B, and randomly decide “A is the seller and B is the buyer”.

d.

The transaction was realized when the generated commodity price (PCom) was
below the CC balance. If the buyer runs out most of his/her, the balance of CC
was stagnant because the transaction could not be performed if the commodity
price was higher than the balance of CC.

9.

The adjacency matrix was assumed to be calculated twice a day within 50 days and hence,%%%%%, as shown in Figure 3.
the average of those two times was calculated and represented as 𝐴𝑑𝚥

2.3.2

The Formation of Customized Community Concept

The concept of “customized community” was implemented in the simulation by creating a market in thecommunity by gradually redistributing the resulted stagnation amount during a 50-day period to themembers who were frequently engaged in the transactions in the subsequent 50-day period. This wasachieved by giving a bonus premium amount when those members purchased CC.
In other word, the premium amount to be awarded is calculated by the initial amount of money (Ini) of
the previous period of time (T) plus stagnation amount from the previous period of time divided by the total
number of members who were engaged frequently in that period. Thus, the initial money at the beginningof each period of 50-day period will be defined by Eq. (2), as shown below:*+,-./,-0

𝐼𝑛𝑖()# = 12.+4 '6'7628 !9& 1/ +𝐼𝑛𝑖( where 𝐼𝑛𝑖# = 10000 , ∀𝑘=1,…,4,(2)!

As can be notices in the denominator of Eq. (1), we used the number of members who were involved in
the non-stagnation group (i.e., NS Group) because those members have the highest participation rates fortransactions within the community.

2.3.3

Random Network Simulation

The simulation was developed by using Python. As mentioned in the previous section, the price of thegoods and services (PCom) was randomly generated in the range of 50 <PCom ≤ 𝐼𝑛𝑖& , ∀𝑛 = 1, … ,5, asshown in Figure 3. This simulation was performed by assuming that the community size consisted of 100
members (nodes) and the transactions were done among them using adjacency matrix (𝐴𝑑𝑗), where eachvalue in 𝐴𝑑𝑗 represents the volume of transactions between each buyer and each seller in the community(see Eq. (1) ), as shown in Figure 4.
Since that 𝐴𝑑𝑗 was generated two times per a day within the 50-day period, the average of 𝐴𝑑𝑗 wascalculated as shown in Eq. 3.
%%%%%
𝐴𝑑𝚥& = 0.5 ∗ (𝐴𝑑𝑗# +𝐴𝑑𝑗$ ) , ∀𝑛 = 1, … ,5

(3)

The stagnation amount was defined as an amount of CC which was not used in the transactions of thecommunity during a period of 50 days. To determine the amount of stagnation, we assumed the buyer who
is the member of the community is interested in the offered market, and he/she will buy the commodity
whose price is PCom (PCom is generated randomly).
Amount of stagnation (𝑆𝑡𝑎𝑔' ) that corresponds to each group was computed using Eq.4
𝑆𝑡𝑎𝑔' = 𝑁𝑟' × 𝑃𝑅𝒎 × 𝐼𝑛𝑖& ∀𝑚 = 1,…,6 and ∀𝑛 = 1,…,5

Table 3：Stagnation amount in terms of CC for each group in 𝑇#(4)Group NameGroup AGroup B
Group CGroup DGroup EGroup NS ※Group Index（m ）The initial amount(Ini)

Participation Rate
(PR)

1
10000
2
10000
3
10000
4
10000
5
10000
6
10000
Total of Stagnation (𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔! )

90%
80%
70%
60%
50%
100%

Amount of
Stagnation（𝑺𝒕𝒂𝒈𝒎 ）

10000
20000
30000
40000
50000
50
150050※The transaction will be realized when the price of the commodity is less than the remaining initialamount of CC (Ini) and hence the price of commodity will be deducted from the initial amount of CC (Ini).
If the buyer used up most of the initial amount of CC (Ini), by making many transactions where each one
was within the limit of the initial amount of CC, the remaining amount of CC will be considered as
stagnation, as shown in Group NS in Table 3 where the stagnation amount was 50 CC .
If the remaining amount of CC is less than the generated PCom, this means that the participant cannot
buy the commodity and the transactions will not be realized. By contrast, if some members in thecommunity are not interested in the offered market as much as others, they will keep some of or all of CC
and this amount of CC will also be considered as stagnation. Thus, the participation rates (PR) of themembers will be reduced and hence different ratios of stagnations will result and the flow of currency
among the members of the community will slow down accordingly.
The total amount of stagnation which resulted from the amount of stagnation from each group wascomputed using Eq.5, and then to be used thereafter to compute the ratio of stagnation relative to overallmoney stock as shown in Eq. 6.
𝑇𝑜𝑡𝑎𝑙𝑆𝑡𝑎𝑔& = ∑;'<# 𝑆𝑡𝑎𝑔' ,𝑆𝑡𝑎𝑔𝑅𝑎𝑡𝑖𝑜& =2.3.4*+,-./,-0"=00260-,9+& >+&6? /,+@(∀𝑛 = 1,…,5(5), ∀ n= 1,…,5(6)

Shannon Entropy(SEn)

Shannon entropy (SEn) is a measure of predictability and is closely related to the probability of arandom variable. The higher the participation rate, the lower the entropy, and the lower the participation
rate, the higher the entropy. This is because if the probability of a specific variable is small, thepredictability will be small, and the entropy value will be high.
Conversely, if the probability of a specific variable is high, the predictability will be high, and theentropy value will be low. Calculating a network map of participation rates for transactions among all
participants in the community will give us an idea of how often buyer and seller transactions are taking
place. This was because the stagnation problem was considered as a result of a decrease in the participation

rate in transactions within the community, so the retention location can be visualized as shown in Fig. 5.
The SEn of the random variable X can be defined as in Equation 7.
Here, 𝑃9 was defined by Equation 8, 𝑥9 indicates the i-th possible value of x among the r symbols, and𝑃9 indicates the possibility of X=𝑥9 .

𝐻(𝑋) = 𝐻(𝑃! , . . . , 𝑃$ ) = − ∑$#&! 𝑃# 𝑙𝑜𝑔% 𝑃#(7)

𝑃# = 𝑃𝑟(𝑋 = 𝑥# )(8)ShannonEntropy
Color Bar

BuyersGroup AGroup B
Group CGroup DGroup ENo Stagnation

Sellers

Figure 5 Color map of Shannon entropy for all the transactions during the simulation(Alaraj, M. and Nishibe, M., 2020)
Figure 6 represents the schematic representation of the “customized community”, while Figure 7
represents the flowchart of the overall method of creating the “customized community”.
All the results were reported in (Alaraj, 2020), which indicated that the ratio of stagnation was decreased
from 15% to 3% after implementing the customization community-related concept (Alaraj, 2020).

Group C
Nr= 10 PR=70%

Group D
Nr= 10 PR=60%

Group E
Nr= 10 PR=50%

When the transactions are executed
among the members for 50-day period,
the amount of stagnation is calculated.

Virtual Community
Nr = 100 persons

Group NS
Nr= 50 PR=100%

Group A
Nr= 10 PR=90%

Group BNr= 10 PR=80%

Figure 6 The schematic representation of the “customized community” during the simulation

StartRecruit members who would like to purchase the CC
Participants purchase CCs with legal currency

The transactions are performed among the members of the community

Generate adjacency matrix (𝐴𝑑𝑗) and compute 𝐴𝑑𝑗(The current 𝐴𝑑𝑗 was created 2 times per a day within T period)

Wait for a time (T)Yes

Has time (T) finished?
Yes

No

Does member use up

Nohis/her CC?

Compute Shannon Entropy (SEn)color map

Stagnation state isoccurredCollect and analyze the information
about members who are engaged
frequently in transactions

Yes

Will member continue inNocommunity?

Receive premiums
on purchase CCs

Recruit members based onEnd

the collected information

Customized Community Formation

Figure 7 Flow chart of the overall method of creating customized community

3. The Method of Constructing “Customized Community”
3.1 C.C.Wallet Platform
In the current research, all the data was obtained from C.C.Wallet platform. C.C.Wallet is a CaaS
(Currency as a Service) platform where users can issue and manage various community currencies. This
platform enables regional development organizations and communities to design and manage depreciating
currency and LETS-based metric currency based on the related communities and organization’s
characteristics and needs by using a mobile application to create a new sustainable society. Additionally,
this platform costs low or free of charge and equipped with the following functions:(1) Transmission Function for QR codes.
(2) Messaging Function.
(3) History Function.
This platform has been examined by using it in various regions and organizations in Japan(Maeda, 2018),
and we will report the main operation of these local currencies.

(a) Local Currencies Info.

(b) Send Points!

(c) Earn Points with your(d) Participate in Reshaping

Figure 8 Community Currency Smartphone Application (C.C. Wallet) Screenshots(Maeda, 2018)
The main screen of the C.C. Wallet application is shown in Figure 8 (a), where any region, shopping
district, company, or any organization can request to set their own currency, issue, and operate it. Also, we
can also see in Figure 8 (b), currency amount (i.e., number of points), message and textbox for the addresseeof the destination. In Figure 8 (c), we can see different skills/activities that the user has registered on theapplication like Herbology Class, 800pt, Chest Art Experience, 1500pt, Haircut, 2000pt, and pick-up fromsome place, 1000pt. Finally, the QR code which is used for transmission. can been seen in in Figure 5 (d).
C.C.Wallet is available and can be download from Apple’s App store or Google’s Play store. The full details
about C.C.Wallet as well as the implementations of this platform are reported in (Maeda, 2018).

3.2 The Transaction Data
All the data was obtained from Global Communications Planning Co.Ltd. (hereafter abbreviated as GCK).
The data was exported by C.C.Wallet as a CSV file form and the template of the file is shown in Figure 9.
1
A

2
B

3
C

4
D

5
E

6
F

7
G

8
H

9
I

10
J

11
K

Currency
Code※１

Time
Stamp

C2C

GPS
Data

Trans.
Point

SellerName

SellerBalance

SellerMessage※2

Buyer
Name

Buyer
Balance

Buyer
Message※１：00112: Currency of Chiba Prefecture※2：almost null

Figure 9 The CSV template of C.C.Wallet
The data file exported by C.C.Wallet platform was not only related to bilateral transactions of
products and services which were purchased by users of C.C.Wallet (member of community), but also to
the transactions at CC malls (CC-based malls) as shown in Figure 10.
GCK Employees

CC-based Mall

(E&F)is thedestination of spending
of CC
F

E

Table 4 The Transaction Data
User

D

B
A

Total Nr.

Total Nr.

Period ofTransactions

Nodes

Transactions

27968

738

2019.5.9 ～ 2021.3.16

C(B&D) is the sourceof acquiring CC
GCK Employees
A.
B.
C.
D.
E.
F.

Health Check App.

Offering products or services
Receive CC regarding products or services
Performing health check daily
Receive CC regard performing health check
Purchasing the offered products through CC-based Malls
Purchasing the offered products or services through bilateral transactions

Figure 10 CC Flow Paths
The flow of CC is shown in Figure 10, while the details of transaction data is shown in table 4.
Specifically, the direction of blue arrows was used to represent the sources of CC where the CC can beearned throughout the bilateral transactions or regarding user’s cooperation in recording his/her healthstatus before starting the work on daily-basis as one of the countermeasures against Covid19, In this regard,
the employees of GCK earned 5 points of CC every day when they record their health conditions such as
body temperature or compliance with rule of washing the hands for 30 seconds or more etc. in the terminal
where health check application is installed in, as shown in Figure 11.
On the other hand, the direction of red arrows was used to represent the destinations where CC can bespent. The green direction points to the products and services which are provided by the user.

Figure 11 The graphical user interface of health check application in GCK
Since C.C.Wallet does not have numerical data that directly indicates the satisfaction of the user, such as
five stars for indicating the fully satisfaction regarding the offered product or services, it is necessary to
predict user satisfaction in numeric form to determine the most popular product and services within thecommunity to be used thereafter as a tool to construct the “customized community”.
Thus, it was first necessary to grasp the name of the purchased product and services not only from what it
was written directly in the comments of the member (i.e., Direct Transaction Trust “DTrust”), when he/she
finished the transaction, but also, we need to calculate how many times that a particular product or service
was purchased (i.e., Indirect Transaction Trust “InDTrust”). In the current research, the DTrust and
InDTrust could be obtained when the user finished the transaction with another user through C.C.Wallet.
As a result, DTrust and InDTrust could be used as indicators for the estimating the degree of satisfaction of
the user regarding the offered product and services, while InDTrust could be obtained only when the userpurchases a particular product from CC-based mall.
The network of transactions can be represented by a directed graph as shown in Figure 12 where eachnode represents a user and each edge represent a transaction. All the details of this network are shown in
Table 4.
In Figure 12, the source of the arrow represents the “buyer” and the target of the arrow represents the“seller” (i.e., “buyer” → “seller”). In graph theory, the number of edges pointing out from a particular nodeis called the “out degree” and hence, the user who has more “out degree”, he/she has purchased many
products/services more than other users.
Considering this, since that GCK gives CCs for each employee who records his/her health status, theoutdegree of GCK will be more than any another ordinary user as shown in Figure12.

GCK

Figure 12 The network of the transactions of C.C.Wallet
3.3 The Analysis Method
To analyze the comment text entered in C.C.Wallet, it is necessary to use the technology of Natural
Language Processing (NLP). NLP is a scientific discipline that aids computers to understand humanlanguages seamlessly. The ultimate objective of the NLP techniques is to extract meaningful informationfrom human languages. Thus, to extract the meaningful information from the comments which were enteredusing Japanese language by the users (members of the community), those comments are needed to be
tokenized (i.e., divide) into tokens by using python language-based module called “nagisa”, as shown in
Figure 13 (A).
Next, based on the entered impression and the number of purchased of the same products or services,
we can estimate the satisfaction degree of the user using 5-stars scale from “5” stars to “1” star, as shownin Figure 13 (B). However, when the user did not evaluate the purchased products, “0” was used as an index
indicating “no evaluation” instead of satisfaction degree.

The analysis process was focused on the 11th field of CSV-related template as shown the Figure 9 where
the user (member of the community) records his/her message regarding the purchased product or services
when the transaction was done between user and another user.
On the other hand, since CC-based malls are registered as users in C.C.Wallet, the sentence in 11th fieldof the generated CSV-related template was consisted of the following format:“User X purchased goods Y at the mall.”, where X represents the name of the user while Y represents thename of the product.
Thus, the name of the buyer and the name of the purchased products were extracted from the 11th field.
User

C.C.Wallet

CSV File・ Aggregate by user
・ Tokenization (split)
・ Integrate the same word into one word

Dictionary
Impression, Products,
Services
Converts into
Numeric Form

User A evaluates the product/service of user B

SellerUser B

Products
orServices

Buyer
User A(B) The scheme of evaluation process

Utilize machine learning (ML)related algorithms
Results(A) NLP-based process

Figure 13 The process of NLP analysis as well as the representation of user’s evaluation
Contrary to what happens between in the transaction between one member and another within thecommunity, the member cannot record his/her impression when he/she bought a product from CC-basedmall.
In the current study, we did not consider the price of the product/services as purchasing something
expensive does not necessary mean that the user satisfy with that product/services while repeating thepurchases process of the same product or service means so and hence, to predict the satisfaction degree ofthe purchased product, the number of times that the user repeatedly purchased the same product (InDTrust)
was considered an indicator to the satisfaction degree which was evaluated on a five-point scale from a star
“5” to a star “1”. For example, if the same product is purchased once, the satisfaction degree is set to “1”,if the same product is purchased twice, the satisfaction level is set to “2”, and if it reaches 5 times or more,
the satisfaction degree will be set to “5” and so on.

Table 5 The list of impression-related words in Japanese with its translations
Highly Satisfied

広め:WideSatisfiedRelatively

Not

Not allNo

SatisfiedSatisfiedSatisfiedimpression

１

０

４

３

あ り が と う:

ご め ん:恐２喝:

く そ: notThank you

Sorry

Extortion

appropriate

わざわざご⾜労: Take the trouble

よ ろ し く: Thankすみ ませできない: Iや

to work

you

ん: Sorry

can notdonʼt like宜 し く: Thank

すい ませ

何 と な く:you

ん: SorrySome how

本当 にあ り がと う: Really Thank有 難 う: Thankあ げ る:

you

you

give

わざわざ: Take the trouble

礼: Thank you

サ ン キ ュ: Thankyou

世話: Looking After

楽しん: have fun

嬉しい: Happy

楽しみ: have fun

⽢く: Sweet
さすが: As expected
⼼遣い: Thanks for consideration
⼤好き: really like
ちそう: Hospitality
馳⾛: Hospitality
Go
貴重な経験: Valuable Experience

美

味

し

い:

Delicious
美味し: Delicious
美 味 し か っ: wasDelicious
美

味

し

く:

と

う:

Delicious
めでCongratulations
疲れ:Effort
久し: After a longtime
頑 張 っ: Do yourBest
お試しに協⼒して

親切: Kindness

くれてありがと
う: Thank you foryour cooperation
確認が取れました。ご協⼒ありが良い: goodと う ご ざ い ま
す ！: Thank you
for

your

confirmation
⾯⽩い: Interesting

おいしかっ: It
was delicious

感謝: Appreciated
もう⼀つ: Another one
ウインナー: Wiener
最⾼: Best
やりたいなぁ: I want to do it
早速: Immediately

⽴派: fine

だ:I3.3.1 Regression Analysis
Regression is a method used in statistics to predict continuous variable and it is also used in the field ofmachine learning. Regression analysis measures “how an increase in one variable x affects another variabley”. In regression analysis, for a given two variables x and y, it is necessary to make a clear distinction
between x “explanatory variable”, and variable y which is called an “objective variable”.
Since that “explanatory variable” is used in the learning process to generate the objective variable, we needfirst to determine what are the “explanatory variable” and “objective variable”?
The “explanatory variable” is determined from asking the following question, “What should we use topredict something?”, while “objective variable” as its name implies, is determined by specifying the
purpose of regression or operational needs.

Name of Variable
Satisfaction
Repetition

Table 6 The learning data of AI-based model
Meaning of Variable
Type of the Data
Type of Variable
The satisfaction of the user
Objective Variable
Numeric
The number of purchased item
Explanatory Variable

To use “objective variables” in the model, “impression-related words” (i.e., words expressing
satisfaction) which was recorded as comments in Japanese language (DTrust) after finishing thetransactions in CCWallet, was extracted as shown in Table 5. Those impression related words were linked
to the satisfaction degree which was set to 5 levels (“5”: highly satisfied, “4”: satisfied, “3”: relatively
satisfied, “2”: not satisfied, “1”. not all satisfied, “0”: No satisfaction-related words).
The full list of Japanese impressions-related words was classified in terms of satisfaction degree which
was five-point scale as shown above in Table 5.
In the current study, we considered the category of the product rather than the offered product itself as
well as the category of the service rather than the offered service itself as described above.
If a multiple “impression-related words” was recorded regarding multiple products/services where eachof which was purchased throughout different transactions as well as classified in the same category, andthose “impression-related words” had different degree of satisfaction, the average of those satisfaction
degrees was calculated. For example, if the impression of a particular user (member of the community)
regarding a particular product was “I really like it” which has a satisfaction degree “5”, and then in anothertransaction, the impression of the same user regarding a particular product whose category was the same ofthe previous purchased one, was “thank you”, which has a satisfaction degree “4”, the average of the bothsatisfaction degrees was calculated and hence, “4.5” was considered a satisfaction degree regarding thepurchased product’s category.
However, since the impression-related words cannot be grasped through the transactions in CC-based

mall, we calculated how many each user bought the same products (InDTrust) from CCs-based mall, andwe considered such repetitions of purchases for the same category of the product as an indicator to user’s
satisfaction level regarding the purchased product.
Thus, the fixed format sentence (“User X purchased the goods Y in the mall”, where X represents thename of the user(member of the community), while Y represent the name of the product’s/service’s
category) which is recorded when the user of C.C.Wallet purchases a product from CCs-based mall in the
11th field of the CSV file, was extracted and evaluated on a five-point scale from the star “5” to the star “1”according to the number of purchases of the same product (InDTrust), and the satisfaction degree of theuser who purchased from CC-base mall only can be computed by the following formula.

𝑇ℎ𝑒 𝑠𝑎𝑡𝑖𝑠𝑓𝑎𝑐𝑡𝑖𝑜𝑛 𝑑𝑒𝑔𝑟𝑒𝑒 𝑜𝑓 𝑢𝑠𝑒𝑟 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑟𝑐ℎ𝑎𝑠𝑒𝑠 𝑜𝑓 𝑠𝑎𝑚𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑡∀𝐼𝑛𝐷𝑇𝑟𝑢𝑠𝑡（1≤ 𝐼𝑛𝐷𝑇𝑟𝑢𝑠𝑡 ≤ 5）

(8)

In the current study, the number of purchases of products and services were extracted per users as shownin Table 7 and Table8.

Table 7 The method of extracting the number of purchases for the products andimpression-related words per user
Product

𝑈𝑠𝑒𝑟#

𝑈𝑠𝑒𝑟$

・・・

𝑈𝑠𝑒𝑟&

Name

𝑃𝑟𝑜#

𝑃𝑟𝑜$

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#・

For 𝑈𝑠𝑒𝑟$

𝑃𝑟𝑜" and

𝑃𝑟𝑜" and・

𝑃𝑟𝑜" and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」・is extracted

is extracted「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#・

For 𝑈𝑠𝑒𝑟$

𝑃𝑟𝑜# and𝑃𝑟𝑜# and・

𝑃𝑟𝑜# and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」・is extracted

is extracted

・

・

・

・

・

・

・

・

・

・

・

・

・

・

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#

𝑃𝑟𝑜'

𝑃𝑟𝑜% and

𝑃𝑟𝑜% and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」

is extracted

is extracted「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

・For 𝑈𝑠𝑒𝑟$
𝑃𝑟𝑜% and・・・「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

Where m represents the total number of products and n represent the total number of users who purchased
the products.

Table 8 The method of extracting the number of purchases for the services andimpression-related words per user
Product

𝑈𝑠𝑒𝑟#

𝑈𝑠𝑒𝑟$

𝑈𝑠𝑒𝑟X

・・・

Name

𝑆𝑒𝑟#

𝑆𝑒𝑟$

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#・

For 𝑈𝑠𝑒𝑟&𝑆𝑒𝑟" and

𝑆𝑒𝑟" and・

𝑆𝑒𝑟" and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」・is extracted

is extracted「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#・

For 𝑈𝑠𝑒𝑟&

𝑆𝑒𝑟# and

𝑆𝑒𝑟# and・

𝑆𝑒𝑟# and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」・is extracted

is extracted

・

・

・

・

・

・

・

・

・

・

・

・

・

・

For 𝑈𝑠𝑒𝑟"For 𝑈𝑠𝑒𝑟#

𝑆𝑒𝑟Z「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

・For 𝑈𝑠𝑒𝑟&𝑆𝑒𝑟' and𝑆𝑒𝑟' and「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」

𝑆𝑒𝑟' andis extracted

is extracted

・・・「𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 −𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑑」is extracted

Where p represents the total number of products and q represent the total number of users who purchased
the products.

3.3.2 Neural Network Model
Typically, there are various types of neural networks (hereinafter abbreviated as NN), but the simplest
form is a three-layer feedforward neural network as shown in Figure 14.
Middle Layer

Input Layer

Output Layer

Activation Function

Activation Function

Figure 14 Three-layer feedforward neural network

3.3.3 The Theme of NN
In the current study, learning process of NN model was performed on the hypothesis that the degree ofsatisfaction regarding the offered product/service can be predicted based on user’s number of purchases
and hence, the goal of learning is to derive a model that can make such predictions.

3.3.4 The Necessary Steps for Building NN Model
When building the NN model, a series of processes are executed as shown in Figure 15. In the current
study, we will explain those processes as shown in the next section.
Start
Data Exploration
and Visualization
Preparation of
The Training Data
Generate NN
ModelPreparation of The
Verification Data
Confirmation of The
Verification Results
No

Is the NN model
accurate?
Yes

End
NN Model Evaluation Phase

Figure 15 The process of Building a NN Model

3.4 Results
3.4.1 Data Exploration
The obtained data includes some of billing-related information and such data was excluded from theanalysis as it is not related to the scope of the current research.
Each participant (node) trades as a “seller” or “buyer” and the number of sales and purchase transactions
does not always match. In addition, some participants purchase and sell a large amount of products andservices of various types, while others purchase and sell only a small amount of products and services andthe number of transactions performed by each participant was not equal.
Since that there were many products and services, we tried to categorize the products with almost thesame contents and meanings into the same category. For example, all items written in various expressions
such as “pan”, “bread price”, and “pan help” as the names of products are put in the “bread” category.
Similarly, services names like “drinking party”, “dining party”, “banquet”, “evening party”, “dinner” and“second party”, etc. , were categorized as “Social Gathering” and so on.
Japanese language had 3 writing systems: Kanji, Hiragana and Katakana, and all of them can be used
to write the same word. Since that some of the names of the products and services were written in Kanji,

while the others whose had similar names were written in Hiragana, we need to unify them into one writing
systems and in the current study we chose Katakana system. It is worthy mentioned that to reduce thecomputational time of the processing, it is important to unify and categorize the products and services into
one category and one writing system.

3.4.2 Data Visualization
To better understand the data, we used the Python language to generate statistical graphs related to
products and services. The data identified 107 types of products (including products provided by the mall)and 68 types of services.
The average of purchases for the product and services were shown in Figures 16 and 17 respectively.
In other words, such graphs can indicate to how much such products and services were popular among all
members whose real names were replaced with animal names for anonymization and keeping privacy.
Also, a function to display the number of purchases for a particular products or services for the members
after writing user’s name (i.e., animal name) was developed as well. As an example, Figure 18 is a graphshowing the number of products purchased by a specific user (“fin whale”) at a mall, while Figure 19 is agraph showing the number of products purchased by a specific user (“fin whale”) at a CC-based mall andthroughout the bilateral transaction.

3.4.3 Generating NN Model
To set the learning process of NN by using Python language, we used 2 patterns of NN as shown below:
1.

“1st pattern”: the values of the parameters were set as below:
nn = (hidden_layer_sizes=(2),activation='relu',
max_iter = 10000,
verbose=True,
learning_rate='constant')

2.

“2nd pattern”: the values of the parameters were set as below:
nn = (hidden_layer_sizes= [(2), (3), (4), (5)],'activation':['relu', 'logistic'],
max_iter = 10000,
verbose=False,

The explanation of the parameters is shown in Table 9.

Figure 16: The Average of Purchases of the Products across the Users

Figure 17: The Average of Purchases of Services across all Users

Figure 18: Quantity of Products purchased by user “Fin Whale” at Mall using C.C.WalletFigure 19: Quantity of goods purchased by the user “Fin Whale” in both regular bilateral transactions

Table 9 The Explanation of NN Parameters
The Name of Parameter
hidden_layer_sizes

Meaning

The Meaning of Values

Number of Elements: Number ofcalculations in the middle layer

・ Two in the first layer
・ 2 in the 1st layer, 3 in the 2nd
layer, 4 in the 3rd layer, 4 in the 5thlayer.

Value of each Element: number ofneurons in each middle layer

activation

max_iterSpecifying the activation function

Maximum number of searches
when searching for the optimal
solutionverboseIf -1 is specified, it repeats until it
converges.
Specify whether to output amessage in the process of model
generation

learning_rate

Update the Weight Learning Rate

Since the number of calculations in
the middle layer is two or more, thislearning is called [deep learning].
・relu：ReLu Function (If the input
value is 0 or less, it becomes 0, andif it is larger than 0, the input is
output as it is).
・logstic:
Logistic
Function(calculates the probability value
and classifies it according towhether it is above the thresholdvalue.
The optimum model search process
is repeated up to 10000 times.

・”True”:
Messagewillbedisplayed.
・”False”: Message will not be
displayed.
・The learning rate specifiedin ”constant” is fixedly used, andthe default is here.

3.4.4 Regenerating NN Model
The second NN pattern was then used to rebuild the NN model using similar training data to improve
the accuracy of predictions. However, adjusting the accuracy of the model needs to “tune” the values ofparameters of NN-model. Thus, such “tuning” process was performed using “Grid Search” which is amethod to find the most accurate model by setting a range of values that can be handled (e.g., 0, 1, 2, 3,etc.) for a parameter called α (alpha) used for model generation. Specifically, it is a method of executing
the process of generating a model by sequentially substituting numerical values in a specific range into α
and using the most accurate model among those models as the final model.
When performing grid search in Python language, we used a module called GridSearchCV. In thefollowing processing, the parameters (setting values used for model generation) for generating the optimum
model were obtained. For example, in the case of “fin whale”, the parameters value necessary to generatethe optimum model were obtained, but such values were different for each participant.
from sklearn.model_selection import GridSearchCV
parameters={'activation':['relu', 'logistic'],'max_iter':[10000],'verbose':[False],'random_state':[4],'hidden_layer_sizes': [(2), (3), (4), (5)]}cv = GridSearchCV(estimator=MLPRegressor(),param_grid=parameters)'activation': 'logistic','hidden_layer_sizes': 2,'max_iter': 10000,'random_state': 4,'verbose': False
Verification with verification data was also performed using the newly generated model. Root Mean
Square Error (RMSE) was used as an index to of measuring the efficiency of grid search algorithm and thevalue of RMSE is shown in the belowTable 10.

Table 10: The value of RMSE when the satisfaction degree of “Fin Whale” isestimated using “2nd pattern”
Type of Transaction
Products

RMSE
0.722

Services

1.380

4. Discussion
So far, a community currency based monetary system has been proposed to strengthen cooperation
between members by repeating non-commercial and commercial transactions among community members,
but sometimes the members cannot find the desired products or services. Consequently, the CC acquired ascompensation for non-profit activities stays in the hands of the members and does not circulate in the system.
If such a situation occurs frequently, a stagnation problem will arise, and a new mechanism is needed tocirculate CCs more smoothly and quickly to circulate CC among the members of the community.
Therefore, we proposed a method so that the feasibility and sustainability of the CC as a monetarysystem can be enhanced by introducing the concept of a “customized community” where commercial andnon-commercial transactions can be integrated.
A manual dictionary was created by using C.C.Wallet users' messages (evaluation comments) wheresatisfaction-related words (i.e., impressions) regarding the offered products and services are recorded after
completing the transactions in Japanese language. Thus, when a new data that did not exist before, it wasnecessary to update the dictionary. Moreover, if there are mistype-related mistakes in the evaluation
messages recorded by members, it is necessary to manually correct those words before using them in thedictionary (e.g., “pin batch” should be “pin badge”).
Thus, as can be noticed, creating this dictionary takes time, but from the viewpoint of privacy protection,
we did not use cloud-based services.
To use the NN model as a tool for predicting participant satisfaction, it is necessary to validate theresults obtained by deep learning using validation data that is different from training data (training data).
By this way, the obtained data needs to be divided into two sets, a training set and a verification set. Withreference to the obtained data, the users of C.C.Wallets were divided into three groups, Group A, Group B,
and Group C, according to the number of transactions.
Users in Group A had enough transactions, and we were able to divide the data into training sets andvalidation sets to build an NN model. Users in Group B did not make many transactions as participants inGroup A and hence, only validation data was synthesized (i.e., not actual data).
On the other hand, Group C is a group of participants whose number of transactions is insufficient todivide into a training set and a verification set (see Figure 12 for members with a very small number oftransactions), and an NN model could not be constructed.
Therefore, to be able to configure a “customized community”, it is necessary to increase the number ofCC users and increase the frequency of transactions and this task was left as a future research topic.
Typically, NN model does not have a general format or optimal values for the parameter of NN in theintermediate layers (hidden layers). Since the purchasing behavior was related to the number of purchases
for the same product/services which differs from one user to another, it is necessary to specify theappropriate NN parameters for each user.
In the current research, the satisfaction of the member was defined in terms of number of purchases ofthe same products or services as the member will repeat the purchase process as long as he/she satisfied
with the offered product/services. Also, such satisfaction was expressed explicitly in the evaluation message
in terms of linguistic expression.
Also, the price of the product/services was not considered as an indicator for the satisfaction of the user
because buying something cheep does not necessarily mean that he/she satisfied with the offeredproduct/service and hence, the price of product/services was not used as an explanatory variable of thesatisfaction in this study.
Since, the name of products/services as well as member’s expression were not frequently expressed in
the evaluation messages of C.C.Wallet platform, the test data was mainly synthesized. In this study, Since
the number of users was relatively large, the computational time was shortened by reducing the number ofintermediate layers of NN model as much as possible.

4. Conclusions
This study proposed a new method to accelerate the circulation of CC among the members of thecommunity by constructing a “customized community” through estimating the satisfaction degree of themembers based on NN model.
First, the satisfaction level was predicted by the comment text recorded by the member of thecommunity throughout the C.C.Wallet. Specifically, it was executed by creating a word dictionary from thecomment sentences entered by the participants after closing the transaction, thereby converting themeaning-based text into numerical values corresponding to the meaning of each word.
The NN model was constructed to estimate the satisfaction degree where the objective variable wasderived based on impression-related words after converting those words from linguistic text to numericvalues where five levels of numerical values (the number of stars from 5 stars to 1 star) based on their
meaning in Japanese language was considered.
On the other side, determining the explanatory variables was rather complicated. It was necessary to
determine the explanatory variables after considering various hypotheses because explanatory variables
were considered as s variables that assist us to explain the objective variable (i.e., satisfaction degree).
Therefore, for each member, the number of purchases of the same category of products/ services were
calculated. Since the high and low prices of products/services do not necessarily reflect the satisfaction of
the participants, the prices of products/services were not used as explanatory variables in this study.
In the current study, the estimation of satisfaction of the members was considered as a bridge to build aprinciple of “customized community” where the circulation of the CC was accelerated based on our
simulation. Since that the purchase behavior was different from one member to another, we need to tune
the values of parameters of NN and hence, the algorithm of “grid search” was used. However, somemembers did not have many transactions as others, and such situations disable us from building NN models.
Also, some members who did not have many transactions to divide them into a training set and a validation
set, the validation set was synthesized for such participants only.
Therefore, if the use of CC is expanded by using various other applications that can acquire CC topurchase products at CC malls, actual data can be increased, and more appropriate results can be obtained.
For example, a new health care application called NUCADOCO as shown in Figure 20, will be released
soon for participants who are implementing health management.
Also, to increase the number of participants who use CC in their transactions, we don’t only need to use
other applications like NUCADOCO, but also we need to offer a wider variety of products in the mall.
In this study, the concept of “customized community” was introduced as a tool to attenuate thestagnation problem in local economics. However, since the obtained data was not sufficient, we decided toutilize simulation experiments to indicate the efficiency of this concept in reducing the stagnation problem.
Thus, building a “customized community” using real data and proofing its efficiency is needed to beinvestigated using actual data obtained from empirical experiments and this task was left for future research.

Figure 20 NUCADOCO Application

Compliance with Ethical Standards
1.

Disclosure of potential conflicts of interests
This research was performed based on a mutual research collaboration between Global
Communication Planning Co. Ltd. and Good Money Lab in Senshu University.

2.

Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection andanalysis were performed by Dr. .Maen Alaraj. The first draft of the manuscript was written by Dr.
Maen Alaraj and Prof. Makoto Nishibe commented on previous versions of the manuscript. All authors
read and approved the final manuscript.

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