The International Workshop on Agent-Based Modeling and Simulation for Policy Development

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AbstractThis paper presents an agents based modeling and

simulation of Indonesian rice price system. I view Indonesian rice

prices as individual subjective estimates of payoff-maximizing

exchange ratios, and hence are as private information of

individual agents who are trading in Indonesian rice market. In

our model, those agents i.e., farmers, small rice-stock traders

(small traders), big rice-stock traders (big traders), and BULOG

(Indonesian logistics national company) presumably produce,

exchange, and consume two goods: imported rice and national

rice. We treat this system as complex, dynamic, nonlinear system

in which agents have limited information. The system evolves

rapidly from an initial random seeding of private prices towards

what may be termed a private but common price structure. This

model provides a general, decentralized disequilibrium

adjustment mechanism that renders market equilibrium

dynamically stable in a highly simplified production and

exchange economy. Through this study, I seek to find and

analyze patterns and determining policies that will help the rice

prices moves through time towards the market clearing price

system for the underlying general equilibrium system, thus our

economy may enjoy rice price stability.

Index Termsagents based modeling, Indonesian rice price,

decentralized bilateral exchange

I. INTRODUCTION

urrently, Indonesia holds about 9% of total world rice

production thus the third largest world rice producer after

China (30%) and India (21%) [1]. Even though, the later

countries are net rice exporter, Indonesia has become net rice

importer since 1980s. Indonesia continuously steps up efforts

to promote its national rice production and to manage national

rice stock for emergency and rice stabilization. Recently,

much complains emerges in public news and media about the

growing reliance of Indonesian rice security policy on import

as shortcut measure and inappropriate pills to reduce

escalating rice prices while disregarding national rice

production capability and Indonesian farmers welfare.

Currently, Indonesia spends about 110 trillion rupiahs for food

import while spends only about 38.2 trillion rupiahs for

national agriculture development [2]. On the other hand,

according to a study [3], 80% of farmers high revenue in

developed countries are due to their governments support

whereas the world low price of rice is not truly reflecting

market efficiency but is already distorted by governments

support and subsidies. This resulting unfair market that is hard

for ordinary farmers in Indonesia to compete.

To face, analyze, and take appropriate decisions on

anticipating this growingly complex and dynamics Indonesian

rice market, a careful simulation and modeling involving

simultaneous exchanges of imported rice and national rice is

needed. The model should provide at least important

parameters and strategies of a roadmap toward stabilizing rice

prices that will be a benefit to Indonesian consumers and

farmers. In this paper, I study the simulation of a particular

economic modelbarter economy. It is a simple economic

model where agents exchange goods without money or other

real-life factors such as firms, taxes, capital or material.

Despite the simplicity, I believe that understanding the

dynamics of the barter economy will be a benefit toward

understanding our real rice market which will help in making

appropriate decisions on controlling the rice price volatility.

One of the important questions in economics has for a long

time been how to match the demand and supply of all goods in

a market of a perfect competition, so that there is neither

excess demand nor supply. Or from another point of view,

how to find the market-clearing prices that would result in

match. The long-run behavior of prices is not well understood

and the issue of which are the main drivers of booms and

slumps remains controversial. For example, the long-run

decline in real agricultural prices is often attributed to weak

demand combined with the production and productivity

increases. Food price volatility is frequently thought of as the

result of droughts and other supply shocks. However, shocks

in the demand for commodities can also trigger price surges.

Macroeconomic policies also play a vital role in determining

price behavior. Speculation is also thought to contribute to

price surges. Nevertheless, on the whole, economist suggests

that none of these factors by itself appears to explain price

behavior satisfactorily. But it was studied that the sources of

volatility of agricultural commodity prices are complex

relations of the volatility of oil prices, interest rates, and

exchanges rates. Most of these factors have been thought of as

crucial in giving rise to the recent price surge. In addition to

these factors, we also need to assess the existence of periodic

form of volatility. Past volatility can be a significant predictor

of current volatility giving rise to periods with either high or

low price volatility. Such volatility patterns are commonly

found in markets where prices are partly driven by speculative

forces [4].

In this paper, I study the particular model which was

formulated by Herbert Gintis [5]. The resulting result of

Mohamad Ivan Fanany (Faculty of Computer Science, Universitas Indonesia)

Agents Based Modeling and Simulation of

Indonesian Rice Price System from

Decentralized Bilateral Exchange

C

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Gintis work is that in a decentralized agents based economy,

where trading agents have neither money nor prices as public

information and with little central control, a system of

approximately equilibrium prices emerges in the long run.

This study provides means to analyze related models by

changing agent as well as market behavior. This model in our

view can be applied to Indonesian rice prices. For this study, I

tune and modify the implementation of an extensible and

scalable agent-based simulation of barter economics [6] which

is based on a multi-agent library MASON (Multi-Agent

Simulator of Neighborhoods) as the underlying simulation

platform [7].

II. PROCESS OVERVIEW

A. Phases

Gintis carried out the following process in each period to

evolve economy over time. It begins with a synchronized

production phaseeach agent starts with an empty inventory

of goods and then produces a fixed amount of a single good.

The production phase is followed by an unsynchronized

exchange-consumption-production phase. Here the agents first

seek exchange partners and then try to agree on the amounts of

exchanged goods according to their strategies. A strategy for

an agent is a price vector for the various goods it produces or

consumes. Agents only give away a quantity of their own

production good and only if the value of what they receive in

exchange are at least as great as the value of what they give

away, according to their private price vector. After a

successful trade, an agent consumes an optimal consumption

bundle and produces more of his production good if his

inventory becomes empty after the consumption.

The final phase is reproduction-mutation, which only

happens after a certain number of periods (for example every

10 period). In this phase, a fraction of low-scoring agents

imitate the strategies (the private prices) of high scoring agents

and with a small probability, each strategy undergoes a small

mutation. This phase can also be seen as the learning phase.

Repeating this process for a long enough time, Gintis showed

that the prices converge approximately to the market-clearing

values thus equilibrium is reached.

B. Generalization and extension of barter economy

The goal of agents based modeling is not to provide as

accurate representation of some real world system as possible,

but rather to enrich our understanding of them. Creating an

all-in-one general model does not take us closer to that goal,

as it becomes harder to grasp what is causing what if the

parameter space grows too large. Agents based modeling

provides easy and safe ways to extend the application by way

of simple, yet flexible interfaces. Generalizing over different

market models could not be done in a way that would allow

for orthogonal extensions. The more general the market

model, the less behavior is pre-defined and the more is left to

custom extensions. Those extensions then become dependent

on each other. A user should be able to change the behavior of

the agents, either for all or for some fraction so that agents

with different behaviors could cooperate in the same

simulation. Gintis original formulation thus becomes a

special case that is implemented as the default behavior.

C. Barter strategies

The trading behavior in Gintiss original model could be

described as purely adaptive; the agents do not take history

into account, nor do they try to maximize their score by

actively adjusting their price levels. By providing an interface

that lets the agent have access to slightly more information

compared to the original formulation, a new set of adaptive

and rational behaviors could be created. In particular, agents

having different behaviors could co-exist in the same market.

The barter strategy interface and an implementation of Gintis

original barter rule is as follows:

public class OriginalBarterStrategy{

public boolan acceptOffer(TradeAgentProxy me,

int offersGood, double offersAmount,

double requestsAmount) {

return !(me.getDemand(offeredGood) == 0 ||

me.getExchangeFor(offeredGood) == 0 ||

me.getInventory(me.getProduceGood()) == 0 ||

me.getPrice(offeredGood) * offeredAmount <

me.getPrice(me.getProduceGood()) *

requestedAmount);

}

}

D. Improvement strategies

Agents improve by being chosen as the worst performing in

a pair. The worse agent then copies the prices from the better

performing agent and possibly adjusts the price up or down by

a fixed factor. Currently, it is has not yet been generalized to

let the user implement new ways for the market to handle

selection. The improvement strategy is implemented on the

agent side, letting the agent have full control over what to do

when it is being selected for improvement. The barter and

improvement strategies can be implemented in the same class

if the need for a strongly optimizing agent arises. The

implemented improvement strategy is as follows:

public class CopyAndMutateImprovementStrategy{

private int numGoods;

private double mutationRate;

private double mutationDelta;

...

public double[]improve(TradeAgentProxy betterAgent,

TradeAgentProxy me) {

double[] new Prices = new double[numGoods];

double[] priceMutation = getMutationVector();

for (int good = 0; good < numGoods; good++) {

newPrices[good] = betterAgent.getPrice(good) *

priceMutation[good];

}

return newPrices;

}

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}

E. Replacement/mutation strategies

After a fixed number of periods, a reproduction period is

added, in which a fraction of low-performing agents of each

producer type are permitted to imitate the behavior of

high-performing types by switching to the price structure used

by the higher-performing types. The implemented

reproduction strategy is as follows:

public class OriginalReplacementStrategy{

public void getNextGeneration(

List producers, BarterParams params,

MarsenneTwisterFast random ) {

long replacements = Math.max(1, Math.round(

producers.size() * params.getReplacementRate()));

for (int i = 0; i

producers.get(k).getScore()) {

producers.get(k).improve(producers.get(j));

} else {

producers.get(j).improve(producers.get(k));

}

}

}

F. Scalability

It is interesting to see how increasing the number of goods

or agents influences the simulation performance. Asymptotic

time complexity of barter economics system is (! +

) where is number of goods, is number of agents per

good, is the maximum number of barter attempts, is

number of periods to run. [6]

III. METHODS

A. Convergence to Private but Common Prices

The original Gintis implementation is as follows. Suppose

each agent produces a certain quantity of one good but

consumes a subset of goods in each period, the particular

goods being re-specified randomly at the start of each period,

except that the agent does not consume his own production

good. Assume each agent consumes in fixed proportions

(!!, , !") such that the utility !!!, , ! in period is

given by,

min !, , ! = min!!

!"

!"#

, (1)

where !! , . . , !! are the < goods the agent consumes in

that period. If agent produces good g, uses prices ! =

!!, , !" and has an inventory !"! of his production good,

he will maximize utility subject to the income constraint

!!"!= !!!! ++ !"!". (2)

The optimum is given by

!!!=

!!

, (3)

where

=

!"!"!

!!!!!!!. (3)

At the start of each period, each agent has an inventory

consisting only on an amount !"! of his production good.

Once in each period, agent "shops," choosing random

producer of each of his non-produced consumption goods and

attempt a trade with each. When he is not shopping, agent i is

"selling," by which is meant that waits to be approached by

the current shopper, and all sellers of a particular good are

equally likely to be approached. When shopper contacts

seller ! , offers to trade good (his production good) for

good (the production good of seller if, according to his

own prices !!, the trade is profitable. The amount traded will

then be greatest amount acceptable to both parties. After a

successful trade, both parties consume any portion of their

inventories that gives positive utility, and if an agent's

production good is depleted, the agent produces a new supply

of the production good. When each agent has completed his

turn as a shopper, the period is ended.

B. Evensen's modifications

Evensen founds three bugs in the original Gintis

implementation. These bugs, however, has no effect on the

price convergence property of the model that could lead to

different convergence behavior. The first bug is related to the

way how agents consume goods. He thought that it is not wise

for agents to consume the whole amount of one particular

good and only a small fraction of some other good. To

maximize the score, agents should try to acquire equal

proportions of all goods. In the original implementation, all

agents will on average score lower because they waste

everything they produce to buy a single or a few other goods

instead of getting a little bit of everything and thus a better

score.

The second bug is related to the calculation of the amount

of their produce-good that they are willing to exchange for

other goods. The calculation is correct for the offerer agent but

is wrong for the responder agent because it accepts the trade

when it values what it receives in trade at least as much as

what it gives up.

The third bug is related to the simulation flow. After each

trading period, Gintis first resets the demand and supply for all

agents. Then, if it happens to be a reproduce period, lower

scoring agents get a chance to copy, or "imitate", the prices of

better scoring agents. After each such period, a bunch of

agents who just got new price vectors will perform trades

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according to the old price vector, because the demand and

supply will not be recalculated until the next period. This also

means trades with negative profit, as did the second bug.

C. Modifications to Indonesian rice price structure

Based on data from BPS (National Statistic Bureau)

Indonesian rice production in average is about 32 millions ton,

where as import and export are about 1.89 millions and 8

thousand tons respectively. This make Indonesian food

self-supply rate about 94.5%. Indonesian government sets

incentives on rice price (HPP: harga pembelian pemerintah)

for domestic rice price. Indonesian rice price is 85% higher

than world rice price. Setting this incentives too high will only

opens higher risks on smuggling and speculations. It is almost

impossible for Indonesian government to fully control the rice

price in the country and prevent rice speculations if

government does not have tools of rice import and export

through tariffs application as protection system. Beside,

government also do not have capability to fully control the

price because rice stocks held by BULOG is relatively low,

i.e., about 0.5 million tons. Thus practically, the rice price in

Indonesia is controlled by trader/miller, especially big traders.

In this study, I want to simulate and model the barter

economy interactions between four main players/contributors

in Indonesian rice markets: Farmers, small traders, big traders

and BULOG, on exchanging simultaneously domestic rice and

imported rice. I ignore the exported rice since the amount is

relatively small hence considered insignificant. Our

assumptions and constraints are as follows. Farmers mainly

produce domestic rice but at the same time also consume and

exchange their rice to other agents. In addition, farmers may

also consume imported rice. Small traders, big traders, and

BULOG also produce both domestic and imported rice either

by buying from other agents or by importing rice. For these

three agents other than farmers I assume that they may

consume the rice by selling it to people which is not included

as one of any agents in this study. The consumption or

volumes of domestic and imported rice for each agent are

made as close as possible to reflect the rice stock census from

BPS.

IV. EXPERIMENTS

In this study, I perform 4 experiments. In the first

experiment, I set the composition of domestic rice and

imported rice consumptions to be the same. Original barter

strategy and improvement strategy (copy and mutate strategy)

are applied. The first experiment is treated as a reference for

other three experiments. In the second experiment, I set the

composition of domestic rice and imported rice consumptions

to reflect the actual data from BPS, i.e., 32.0: 1.89, while still

using the original barter and improvement strategy. In the

third experiment, I retain the consumption rate as the second

experiment, but instead of using the original barter strategy, I

use generous barter strategy set as follows.

public class GenerousBarterStrategy{

public boolan acceptOffer(TradeAgentProxy me,

int offersGood, double offersAmount,

double requestsAmount) { return !(me.getDemand(offeredGood) > 0 &&

me.getExchangeFor(offeredGood) > 0;

}

}

In the fourth experiment, I change improvement strategy from

copy and mutate to mean improvement strategy which is set as

follows.

public class MeanImprovementStrategy{

public double[] improve(TradeAgentProxy betterAgent,

TradeAgentProxy myself,

BarterParams params, MersenneTwisterFast random) {

int numGoods = params.getNumGoods();

double[] newPrices = new double[numGoods];

for(int good = 0; good < numGoods; good++) {

newPrices[good] = (betterAgent.getPrice(good) +

myself.getPrice(good)) * 0.5;

}

return newPrices;

}

}

V. RESULTS AND DISCUSSION

A. Results of the first experiment

Fig. 1. Average Score of agents trading or exchanging domestic and imported rice. The scores converge to relatively equilibrium score after about 5000 period

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Fig 3. Low scoring and high scoring agents (from [6])

Fig. 2. Volatile average rice price stabilizes after about 5000 periods.

Fig. 4. Agents scoring status. All reach high scoring for just about 5000 periods.

B. Results of the second experiment

Fig. 5. Average Score of agents trading or exchanging domestic and imported rice. The scores converge to relatively equilibrium score after about 22500 periods.

Fig. 6. Volatile average rice price stabilizes after about 22500 periods.

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Fig. 7. Agents scoring status recorded in several discrete periods. All reach high scoring after about 22500 periods.

C. Results of the third experiment

Fig. 8. The scores converge to relatively equilibrium score after extremely long, i.e., 125000 period. In addition a price spike seems needed on the domestic rice price to trigger the imported rice price spike that will lead to the equilibrium.

Fig. 9. It took very long and difficult way to volatile average rice price to stabilize, i.e., after about more than 120000 period.

D. Results of the fourth experiment and discussions

In the first experiment, if the composition of domestic rice and imported rice is about the same, the price stabilizes very fast. Of course this is really does not reflect the real condition and it is unlikely that the two are produced and consumed equally since Indonesia is one of main rice producer. It needs only about 5000 period to stabilize. In the second experiment, when I divide the rice composition to the numbers given by BPS, the convergence took about more than 20000 periods. The lesson learned from this is that it takes quite a lot of time for the two prices to converge. In the third experiment, when I change the original barter strategy to generous barter strategy, the convergence took extremely long time to converge, i.e., about 120000 period. The lesson learned from this is that all agents seems better to be more reserved in exchanges than to be more generous given unbalanced goods composition. In addition a price spike seems needed on the domestic rice price to trigger the imported rice price spike that will lead to the equilibrium. I believe more experiments should be performed to confirm these two lessons. In the fourth experiment, when I change the original improvement strategy (copy and mutate) to mean improvement strategy, the convergence took faster time. The lesson might be in case of imbalance composition mean improvement strategy which take more information from all agents is better than the copy and mutate strategy which takes information from only a high performing agent. As conclusion, the agents based modeling and simulation especially the decentralized bilateral exchanges is really helpful and very potential in extracting anticipated patterns toward stabilizing the rice price in Indonesia. Further more in depth study should be promoted and encouraged.

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Fig. 10. The scores converge to relatively equilibrium score faster, i.e., 15000 period.

Fig. 6. Volatile average rice price stabilizes faster after about

15000 period.

REFERENCES

[1] Hussein Sawit, Indonesia dalam tatanan perubahan

perdagangan beras dunia," http://bulog.co.id/old_website

/karyailmiah.php, Majalah Pangan, 2006

[2] Elisha Kartini, 110 Triliun Rupiah untuk Impor Pangan

Vs 38.2 Triliun Pembiayaan Pertanian, (wawancara),"

http://www.csoforum.net/multimedia/bahan-bacaan/435-

110-triliun-rupiah-untuk-impor-pangan-vs-382-triliun-pe

mbiayaan-pertanian.html

[3] Hussein Sawit, I.W. Rusastra, "Globalisasi dan

Ketahanan Pangan di Indonesia", bagian laporan

penelitian Road Map Memperkuat Ketahanan Pangan,

PEM UI, Jakarta, 2005.

[4] George Rapsomanikis, Alexander Sarris, "Commodity

Market Review 2009-2010" FAO Trade and Markets

Division.

[5] Herbert Gintis, "The Emergence of a Price System from

Decentralized Bilateral Exchange", Contributions to

Theoretical Economics, 6(1):1302-1302, 2006.

[6] Pelle Evensen, Mait M a rdin, "An Extensible and

Scalable Agent-Based Simulation of Barter Economics,"

Master of Science Thesis, University of Gothenburg,

Dept. of Computer Science and Engineering, Sweden,

March 2009

[7] Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, and

Keith Sullivan, MASON: A New Multi-Agent

Simulation Toolkit. In Proceedings of the 2004

Swarmfest Workshop, 2004.

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