Decision Model and Simulation for Autonomous Lenient ...wasatiyyah. Hassan (2013) explained that...

MALAYSIAN JOURNAL OF CONSUMER AND FAMILY ECONOMICS Vol 24 (S2), 2020

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Decision Model and Simulation for Autonomous Lenient Bidding in Shariah-based E-Auction

Norleyza Jailani1, Mohammed Al-Aaidroos2, Muriati Mukhtar1 1Center for Software Technology and Management

Faculty of Information Science and Technology Universiti Kebangsaan Malaysia, Selangor, Malaysia 2Seiyoun Community College, Hadhramaut, Yemen

Abstract Auction outcomes can be more efficient when bidders’ preferences are altruistic rather than selfish specifically for sustainable model which go beyond self-interest. This paper introduces a novel altruistic model inspired by the concept of Islamic leniency to promote benevolent acts between auction participants through a bidding strategy that relies on Falah decision model that reflects Islamic leniency values. In this model, the altruistic utility is used to simulate empathy and replace the egoistic behaviour, while lexicographic preference introduces the levels of satiations where the agent stops maximizing his utility. Software agents implemented using the Jade platform are utilized to represent bidders’ and sellers’ behaviours. We conducted simulations to demonstrate i) selfish bidders behaviour, ii) altruism behaviour towards selfish seller in dire need (daruriyat) and iii) altruism towards other bidder who is the current winner. In all selfish bidders simulation, competition increased with number of bidders. In simulation ii) winning prices are higher compared to all selfish bidders since bidders feel emphaty towards seller. Findings from the simulation iii) shows that as altruistic perception increased, auction becomes more efficient since the number of bids submitted decreased which means less competition between bidders even though the other bidders behave selfishly. The simulations on the proposed Falah-based bidding strategy proves its worthiness to soften auction competition and promote Islamic values compared to the conventional utilitarian decision model.

Keywords: Altruism, Bidding strategy, Lexicographic preference, Sharia compliant, Software agent

1.0 Introduction An auction is a process of buying and selling goods or services

by offering them up for bid. From linguistic perspective, the word


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auction comes from the Latin verb ‘auctio’ (from ‘augere’) which means ‘to increase’ and surprisingly the equivalent arabic term for auction al-mazayadah comes from the root word zayadah which also means ‘increase’ (Hultmark et al., 2002; Academy, 2004). Like negotiation, auction use a dynamic pricing mechanism in order to determine price offered by potential buyers. In the pre-Islamic era, auction was prominent for trading slaves (Ali, 2002). The prophet Muhammad (SAW) himself practiced auction to sell a cup and a rug owned by one of his companions (Al-Tirmidhi: No. 13509, 1998). In the last two decades electronic commerce successful take off has positive impact on e-auction advancing it to reach a large number of potential customers and suppliers with lesser cost and time (Lieber & Syverson, 2012). This is exemplified by eBay, the largest online e-auction which recently reported a revenue of 2.88 billion U.S. dollars in the fourth quarter of 2018 (eBay.com, 2019). However, studies on existing online auctions reveal several features which do not comply with Shariah principles such as the existence of usury (riba), gambling (miser), uncertainty (gharar), and price inflation (najash) (Jamaluddin et al.,2011; Al-aaidross et al., 2013; Jilani & Ansari, 2017). Despite the increasing awareness of Muslim communities to abide to Shariah principles, most Muslim marketplaces only focus on trading Halal products rather than the compliancy of the trading process to Shariah principles. Hence it is necessary to refine the e-auction processes, procedures, protocols, as well as decision models to embed the requirements of Shariah.

Software agent plays a prominent role in designing automated bidders in negotiating prices in auction system (Baarslag et al., 2013). By supplying specific configurations, agents are capable of following user’s constraint to bid without any intervention until the auction ends. Keeping in mind that auction is a competitive bargain, homo economicus considered the primary motive for auction participant is the self-desire that needed to be satisfied (Parsons et al., 2011). In order to maximise revenue, a seller in an ascending auction for example tries to gain the highest price while bidders tries to keep the prices as low as possible. Eventually, auction ends at an equilibrium state where both parties arrived at an acceptable level of satisfaction. Following these assumptions, computer scientists were inspired to design efficient bidder agents through employing what is known as utilitarian decision model. Many bidding algorithms depend on utility principles such as utility maximization, monotonicity and non-satiation to attain profit


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maximation. For instance, Kiani & Ansari (2017) proposed an auction-based profit maximation approach for bandwidth allocation amongst subscribers of mobile network services, while Jiao et al. (2017) proposed a Bayesian profit maximization auction which maximizes the data service provider’s profit by choosing the optimal sale price and data size bought from the data collector.

Nonetheless, today’s unpredictable socio-economic environments have called for a more sustainable utility model rather than utility model which is based on consumer satisfaction alone. Sustainable utility model such as Mindful Consumption is premised on a consumer mindset of caring for self, for community, and for nature (Birch et al., 2018). Huang and Kim (2018) also considered maximizing social welfare with less total production cost in online combinatorial auctions. This shows sustainable utility models have also moved towards considering the non-economic values in consumer decision making. Non-economic values such as well-being, partnership, fairness, trust, and justice are becoming more acceptable in services such as Islamic banking (Javed et al., 2013). Islam has its own norms and ethical values with regard to consumer behaviour that leads to seek the priority for the fulfillment of basic needs, fairness distribution of consumption among the family members, spending on religious and social ceremonies, savings for the rainy days and others expenditures (Salleh et al., 2013). Zain (2017) further discussed the concept of seeking the best in connection with sustainability. In his earlier work he argued that the present concept of optimization is based on greed and extremism, or has moulded in those values from democratic (neo)-liberalism, capitalism, socialism, secularism and elitism. Thus, he proposed a new concept of sustainability based on the concept of wasatiyyah. Hassan (2013) explained that wasatiyyah as one of the basic principles in Islam should encompass three attributes namely justice (al-ádl), excellence and goodness, as well as balance/moderation. He also observed that the qualities of justice and goodness/excellence are being neglected, sidelined or forgotten due to the over-emphasis on moderation. Moderation should be interpreted as striving to uphold all that is good and commanded in Islam and to achieve excellence (ihsan) in everything.

The concept of justice and seeking the best is much more important in online auctions because these buyers and sellers are not in physical contact with each other. To ensure justice and excellence are preserved, online transactions should be carried out by maintaining


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balance and equilibrium between the parties involved. The proposed model tries to incorporate the wasatiyyah concept of justice, excellence and balance by promoting the behaviour of lenience and benovalence (altruistic) in designing bidding strategies towards a just transaction between buyer and seller. Sympathetic dealing as said by Prophet Muhammad (SAW) ought to increase both parties’ degrees of satisfaction and pleasure, which is very necessary for any kind of business transaction. Lenience urges for disciplined profiteering without exaggeration and thus makes sellers accept sufficient margins. Leniency if practiced by Muslims traders has great potential towards inviting others towards understanding Islam by exemplifying its true meanings.

Several attempts have been carried out to construct Islamic utility model as shown in Table 1. The model proposed by Zaman (1992) was found to be the only that can be customized to suit automated bidding. This is because it is able to address most of the arguments raised in the conventional utilitarian model which was discussed in our previous work (Al-aaidroos et al., 2016) by introducing the lexicographic preferences, the utility satiation, and the altruistic utility.

Table 1 : Islamic utility models

No. Authors The Model Characteristics Analysis 1

al-Zarqa 1978

- Reward/Punishment representation of the Muslim consumption.

- Define three levels of consumption priorities: Daruriyat, Hajyat, and Tahsinat.

- In addition, defines a level of prohibited extravagance.

Unclear how the author interprets the Muslim’s purchase decision.

2 Bendjilali 1993

- The model differentiates between worldly and spiritual satisfaction.

- The level of Iman used to allocate between each part.

- Define three consumption sets corresponding to Daruriyat, Hajyat, and Tahsinat.

- Utility maximized until a maximum limit of the consumption and if exceeded that utility declined to indicate for extravagance situation

Level of Iman is an internal belief which could not be measured. The method closer to lexicographic preferences.


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Table 1 : Islamic utility models - continue

No. Authors The Model Characteristics Analysis 3 Khan

1995 - Use a two parameter but linear utility

function. - Differentiates between self and others

wellbeing - Closer to altruistic utility but the utility

curve always linear. - The level of Iman increases the

amount of spending for others.

Some conflicts in the mathematics formula. Linear utility curve might not always be true.

4 Zaman (1992)

- Only modifies the conventional utility model to settle conflict with Shariah.

- Introduce three treatments including: lexicographic preferences, satiated utility, and altruistic utility.

Found suitable to apply in automated bidding. Customizable and no immeasurable factors.

5 Salleh et al. (2013)

- Use two dimensional utility. - Differentiation take place between

worldly and ukhrawi commodity set. - The allocation between each set is

taken based on a lexicographic preference decision model.

Ukhrawi commodity set is linked to spiritual aspects such as level of Iman which is difficult to measure.

Inspired by the concept of leniency, this work attempts to

incorporate benevolent practice in automated bidding. The paper proposed the concept of leniency in bidding strategy and analyzed the results. The rest of the paper is organized as follows. Section 2 presents the proposed lenient decision model, while Section 3 explains the conducted experiments and simulation tests with brief discussion on the results obtained. This is followed by the conclusion and future works.

2.0 The Falah Decision Model

Bendjilali (1993) defined Falah function as “a mapping from a set of actions to the set of real numbers in which Muslim individual aims to maximize through the allocation process governed by Islamic norm”. The term Falah is widely used in Shariah literature to express the ultimate success in the hereafter. Islamic economists thus prefer to use the term Falah to indicate success in this life and simultaneously in the hereafter (Azhar, 2010). In auction the Prophet Muhammad (SAW) prohibited the sale of najash in which sellers resorted to collusion with a bidder to raise the price. Thus, leniency is stimulated in order to overcome greed and promote Islamic business ethics as described in


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Kolan et al. (2018). In this model, the altruistic utility is used to simulate empathy and replace the egoistic stimulation from the conventional decision model, while lexicographic preference introduces the levels of satiations where the agent stops maximizing his utility. In an ascending auction scenario, the altruistic bidder agent can submit affordable prices aiming to benefit a needy seller. Altruism can be offered to other bidders by choosing not to outbid their winning bids. Both situations can be demonstrated towards a needy seller or a disabled bidder who badly needs to buy a wheelchair. However, there are circumstances when the decision maker is unable to tolerate. This can be addressed by treating each circumstance differently using two lexicographic levels which differentiate between the essential status (Daruriyat) where only self-interest decision (selfish behaviour) is considered and when altruistic behaviour (Hajyat) can be applied. Given a situation where a bidder specifies the maximum bid he can afford is $500 for any arbitrary auction, let’s assume for prices between the range of $0 to $450, the bidder is able to tolerate to certain extent, thus he can behave altruistic (Hajyat level). If the price goes beyond $450 it is considered expensive and unaffordable, hence he will start to exibit selfish behaviour (Daruriyat). Mathematically, the altruistic utility can be represented with a function of two parts: the self-utility and the aggregate summation of others utilities which represent the degree of altruism towards other beneficiaries. The first part represents the pleasure that an agent received when taking a decision fulfilling his self-desires while the second part represents the pleasure agent receives from sensing others. Therefore, the general form of the altruistic utility of an agent i belong to a community of N members can be defined as follows:

𝑆 = [1𝜇!"𝜇"!1𝜇"#𝜇!# ……𝜇"$𝜇!$𝜇#" ⋮ 𝜇$"𝜇#! ⋮ 𝜇$!1

⋮ 𝑎𝜇$# … ⋮ …𝜇#$ ⋮ 1]Where 𝑈%&'( (𝑥) Represent the self-utility consider it as any secular utility

function. 𝑈%&') (𝑥) Represent self-utility of the other members on the

society. 𝜇() is the altruistic coefficient which can be deduced from the

following matrix.

(1)


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Each row in S matrix represents the degree of altruism a member feels towards other members in the community and is known as the altruistic coefficient. For simplicity the entries of the altruistic coefficient are adjusted to be measured as fractions between 0 and 1 where 1 means fully altruistic while 0 means selfish. The altruistic utility can be seen as a generic utility of the conventional selfish utility which only adjusts the altruistic perception between selfishness and generosity. Figure 1 shows simple visualization of different altruistic utilities by adjusting the altruistic coefficient µ values from 0 to 1. For instance, F0, F1, F2, F3, F4, F5, F6 represent altruistic utilities with µ values of 0.0, 0.05, 0.25, 0.5, 0.65, 0.75, and 1 respectively. F0 represents total selfish utility and F6 represents total altruistic utility.

Figure 1 : Visualization of various level of altruistic utility

Now let’s introduce the second treatment by defining the lexicographic preference. Accordingly, different levels of satiated utilities needs to be defined in which lower level utilities satiated in prior to proceed to the next utility level. As a result, each used utility function shall comprise of satiation condition in which the utility could not exceed. Mathematically, the general lexicographic ranking system can be expressed as follows:


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In its simplest form, let k=2, where we consider there is only two satiation levels representing essential (Daruriyat) and higher level (Hajyat and Tahsinat). Then:

Decision maker at the Hajyat and Tahsinat level can behave

generously but not those at Daruriyat level. The model used three set of equations to create, evaluate, and then decide the best suited action. During the auction run, a bidder agent can autonomously decide whether to submit a bid or not and if decided to submit a bid then bid amount can be computed automatically. Consider a scenario of auction with N participants listed by a seller named slr. If Tmax is the time at which the auction ends, t is the current timestamp defined within the auction period [0, Tmax], and wnr is the current winner with a bid price P t. Now the bidder agent named bdr will take a decision wether to submit a bid or not at the coming time t’ using the following decision function:

Where

Note that, there are only two lexicographic levels defined in the decision function above. The definition of each level can be seen from the entire definition of U1(x) and U2(x) such that:

Where Vbdr(P) can be forecasted with respect to the bidder maximum price such that:

(2)

(3)

(4)

(5)


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In fact, during the bid decision, the bidder agent would concern only with the current winner and the seller welfare. That is simply because other participants have no effect in the bidding decision. Therefore, the altruistic portion of U2(P) could be expressed in details as follow:

In case the bidder decision function result is to send a bid, then

the bid amount Pt will be generated based on the exponential time dependent utility function α(t) such that:

Where: - Pmax is the maximum price a bidder is able to offer - P t’ is the auction price forecasting for that bidder - Pcur is the current price (highest price) - Pinc is the static bid increment as defined in the Table 2

Table 2 : Bid increment values adopted from eBay.com

Condition P>$5000 P


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going to report on the difference between selfish and altruistic bidder behaviour.

Table 3 : Auction item details

Property Product Start price

Reservation price

Bidding protocol

Duration

Details 200 Hadith Book by Abdul Rahim Al Fahim

$20 $120 Ascending open

(English)

7 days

3.1 Simulating Selfish Bidder Behaviour

This test aims to imitate selfish bidder behaviour which reflects the conventional utility model assumptions of self-maximization. The seller is also assumed to behave in a selfish manner. On the contrary, each bidder will prefer lower prices and his utility can be expressed any arbitrary price value as Pt∈[0, Pmax] in equation (5). The altruistic coefficient value is close to zero to represent selfish agent behaviour. In order to inspect the influence of bidders on the competition, the test was conducted in two rounds. Round A involved two selfish bidders where Bidder#1 and Bidder#2 maximum prices were set to $400 and $300 respectively. Round B involved three selfish bidders where Bidder#1, #2 and #3 specified maximum bid prices of $400, $350 and $300 respectively. Table 4 shows the specification and results. Auction ended with Bidder#1 as the winner with bid price of $160 since he specified a higher valuation of the product ($400). Figure. 2 shows the result obtained from the behaviour of Bidder#1. It shows the difference between the ongoing auction price (Pt) and the agent price forecasting (Pt’). The results showed high influence of the selfish behaviour on the final price since each party tried to maximize his self-interest to win at the lowest price possible. This was reflected in the selfish utility function Vbdr() which preserved its maximization goal and ended only at 0.61 out of 1. This corresponded with a very satisfiying win of the highest bid price of $160 although the winner can afford to pay a maximum price of $400. With three bidders in Round B, the competition was slightly increased as shown in the total number of bids submitted 126 and the auction final price which ended at $202.50 won by Bidder #1 due to his higher valuation. The results as shown in Figure. 3 were almost the same as in Round A since all participants behaved selfishly. The final utility of Bidder#1 is 0.51 out of 1 which was still considered satisfactory from the bidder’s perspective.


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Table 4 : Selfish bidder behaviour specification and results

Auction participants

Round A Round B Max bid Behaviour Total

bid sent Max bid Behaviour Total

bid sent Bidder#1 $400 Selfish 55 $400 Selfish 56 Bidder#2 $300 Selfish 54 $350 Selfish 55 Bidder#3 $300 Selfish 15 First bid sent (second)

217728 217728

Last bid sent (second)

592704 592704

Highest bid price

$160 $202.50

Winner Bidder#1 Bidder#1

Figure 2 : Selfish bidder test results Round A


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Figure 3 : Selfish bidder test results Round B

3.2 Simulating Altruism Behaviour Towards Seller

To simplify simulation, this group of tests assumed a scenario of a bidder who is only concern about the seller’s welfare which resembled bidding in charity auction (Leszczyc & Rothkopf, 2010). Other bidders’ welfare are ignored. Only Bidder#1 behaved altruistically towards the seller. Bidder#2 and Bidder#3 behaved selfishly towards the seller. The seller was in Daruriyat situation and had to behave selfishly. The simulation tests were conducted in two rounds A and B as in previous test, the specification was also the same except for Bidder#1’s behaviour. For each round, three experiments were executed to measure different altruistic coefficient μbdr→slr. Thus,

three values measured for μbdr→slr =0.25 (altruistic), μbdr→slr =0.15

(moderately altruistic), and μbdr→slr =0.1 (low altruistic). Because Bidder#1 behaved selfishly towards other bidders then the altruistic coefficient toward the winner bdr→wnr was adjusted to approach zero. In comparison with selfish utility, Fig. 7 shows that altruistic utility is less affected by the prices change especially when the extent of altruism is high, with the x-axis represents time in second and y-axis represents price ($). This means that a selfish agent is very concerned with the change in prices while the altruistic bidder agent does not mind starting bidding at a higher price as he feels compassion towards the seller (i.e utility value is more stable).


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Figure 4 : The forecasted altruistic utility with time

Figure 5 : Experiment E1 Round A (μ =0.25)


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Table 5 : Round A experiments result statistics

Experiment Total bids

Win Price ($)

First bid sent(s)

Last Bid sent(s)

E1(μ =0.25) 9 295.05 12096 598752 E2(μ =0.15) 17 318.56 12096 604800 E3 (μ =0.1) 23 308.82 24192 598752

Figure 5, 6 and 7 show the results for experiment 1, 2 and 3.

Note that in all experiments, the real bidding values (Pt) were in between the altruistic estimation (Palt) and the selfish estimation (Pslf) of the bidder. In all experiments, the win prices (highest bid) were higher than in the selfish behaviour results (discussed in 3.3.1) which


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reflected the influence of altruistic perception in submitting bids in favour of the seller. As the altruistic coefficient increased, the total number of bids submitted throughout the auction decreased, which means that altruism is inversely proportional with competition. In order to compare the result obtained for the three experiments, Table 5 summarizes the statistics obtained from each experiment. 3.3 Simulating Altruism Toward the Current Winner

This test highlights the case when a bidder gives consideration to the other bidder who is leading at the time of making the decision to submit a bid. The scenario demonstrated in this test is aimed to exemplify the Muslim leniency in its highest level as said by the prophet (SAW) “None of you truly believes until he loves for his brother what he loves for himself.” (al-Bukhari: Volume 2, Number 13) (AlBukhari, 1997). This simulation test has been conducted in two rounds A and B. Round A involved two bidders in which one behaved altruistically towards the other, while the second behaved selfishly. The results shown in Figure 8 shows the big influence of the bidder’s altruism in the auction outcome. When adjusting μbdr→wnr =0.1(low altruism), the final price only reached $75 which was very low for this bidder. This is because Bidder#1 gave the priority to Bidder#2 to win and therefore it continuously avoided to outbid Bidder#2’s highest bids. Instead of sending his first bid earlier, he waited until t=423360s. Since both agents exhibited selfish behaviour towards the seller, the price did not go up very much (ended at $75). Furthermore, the number of bids received decreased to 60 bids compared to 109 bids received in the normal selfish test (Section 3.3.1). In round B, three bidders were involved, where Bidder#1 behaved altruistic toward Bidder#2 and Bidder#3 while they themselves behaved selfishly toward the altruistic bidder (altruism assumed not to be reciprocal). Table 6 shows the results for round A and B experiments with varying altruistic coefficient values. Final win prices in round B are higher as competition is increased since a selfish agent was added. The influence of different coefficient µ values on the altruistic utility can be clearly observed in Figure 9. Findings from the simulation shows that as altruistic perception increases the number of bids submitted decreases which means less competition between bidders even though the other bidders behave selfishly. It is also noted that the magnitude of the altruistic agent utility is more affected by the change of prices when


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using less altruistic coefficient values compared to the higher altruistic coefficient values.

Table 6 : Bidder’s altruism test statistic round A and B

Experiment

Round A Round B Total bids

Win price

($)

First bid

sent (s)

Last bid

sent(s)

Total bids

Win price

($)

First bid

sent (s)

Last bid

sent(s) E0 (μ =0.0) 109 160.00 217728 592704 44 190.00 302400 592704 E1(μ =0.1) 60 75.00 423360 604764 26 160.00 453600 604764 E2(μ =0.15) 50 63.00 453600 604764 23 137.50 471744 604764 E3(μ =0.25) 46 61.00 501984 604764 16 132.50 514080 604764

Figure 8 : Result of E1(μbdr→wnr=0.1) of the bidder altruism test


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Figure 9 : The comparison of utilities for different altruistic

perception of bidder’s altruism test

4.0 Conclusion An investigation on altruism and its implications on automated

bidding have been presented in detail. The influence of different altruism coefficient values on the altruistic utility can be clearly observed. The simulation results showed the potential of the Falah decision model to be used in automated bidding agents in order to realize the Shariah-based online auction. Auction becomes more efficient when bidders behave altruistically towards seller and other bidders. In addition, winning prices are higher which do justice to sellers who are in need. This also benefits auction market authorities in generating more profits due to higher auction prices. In future, this research could be extended to explore more advanced bidding tactics such as fuzzy-based or augmentation-based decision models and could be spanned to cover more auction protocols like sealed or descending bidding scenarios.

Acknowledgements

This work is partially funded by Universiti Kebangsaan Malaysia under the KRA-2018-024 grant and Ministry of Education (MOE) Malaysia under the FRGS/1/2014/ICT07/UKM03/2 project.


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