# What is SCM and SCML?¶

The Supply Chain Management (SCM) world simulates a supply chain consisting of multiple factories that buy and sell products from one another. The factories are represented by autonomous agents that act as factory managers. Agents are given some target quantity to either buy or sell and they negotiate with other agents to secure the needed supplies or sales. Their goal is to turn a profit, and the agent with the highest profit (averaged over multiple simulations) wins.

The Supply Chain Management League (SCML) is a part of the International Automated Negotiating Agents Competition (ANAC) since 2019. SCML runs in the SCM world.

The league is built using NegMAS which provides tools for building multiagent systems in which negotiations are embedded within world simulations.

There are two different yet related games in SCML each represented by a NegMAS World class:

• SCML2021World A world representing the complete SCML game described here in which the agent is responsible of defining its own utility function, with who to negotiate, negotiation agendas to propose, production schedule, etc. This form of the SCM world is more representative of real-world trading with a combination of long-term planning, negotiation, and production scheduling.

• SCML2020OneShotWorld A simpler form of the SCM world in which agents do not need to consider long-term planning or production scheduling described here. This form was introduced in 2021.

You can use the SCM world simulation in your own development and research as you see fit but it is primarily intended for the SCM league (SCML) of the ANAC competition. SCML has three tracks:

1. Standard track based on the SCML2021World full game simulation. In the standard track, a single instance of each agent type exists in the world. This precludes any possibility of collusion between agent instances of the same type and focuses on business operation of a single factory.

2. Collusion track based on the SCML2021World full game simulation. In the collusion track, multiple instances of each agent type exist in the world. This opens the possibility for these agents instances to collude in order to corner the market and get higher profits that what each of them can get on its own. In this track, collusion is not considered a form of cheating but is encouraged.

3. OneShot track based on the simpler SCML2020OneShotWorld game simulation. In the OneShot track, a single instance of each agent type exists in the world precluding any possibility of collusion. The focus here is on negotiation with less focus on long-term planning as discussed earlier. This track was introduced for the first time in 2021.

The SCML competition has two versions:

• Online Competition Participants to this competition can submit their agents as long as the competition is running (March 15th 2021 to July 1st 2021). The system runs tournaments between the latest version of all submitted agents for each track periodically and maintains a leader-board showing the top agents updated at least weekly. Participants in this competition will get the chance to observe how their agents behave against agents submitted by other competitors and receive feedback about any exceptions or error caused by their agents.

• Official Competition The final version of each agent submitted to the online competition (and registered before July 1st, 2021) is entered into the official SCM league which will run as part of the ANAC 2021 competition in conjunction with IJCAI 2021. The winners will be announced during the as part of the official program of IJCAI 2021.

# A (not very) brief introduction to NegMAS¶

The SCM world is built using NegMAS. This section provides a very short introduction of it to help you understand the terms used in this document. Please refer to NegMAS documetnation for full description and details as well as tutorials and other goodies.

This image shows the main components of a simulation in a NegMAS world:

The simulation is run using a World object which defines what happens in every simulation step, provides a BulletinBoard object containing all public information about the game, calls various callbacks defined in the Agent object representing each agent in the environment, takes care of running negotiations and keeps track of agreement signing and the resulting Contracts. The World object also controls logging, event management, serialization, visualization, etc. Refer to the World documentation for more details (you need to do that only if you are implementing new world simulations).

The designer of the game implements a World class by overriding few abstract methods in the base World class. This is how the SCML2019World, SCML2020World, SCML2021World and SCML2020OneShotWorld were created. As a participant in SCML, you never need to interact directly with the World object.

The logic of an agent is NegMAS is implemented in an Agent object. The designer of the simulation, should provide a base class for its specific world inherited from NegMAS’s Agent class. For SCML, this base class is called OneShotAgent for the OneShot game (to go with the SCML2020OneShotWorld), and SCML2020Agent for the full game (to go with the SCML2020World and SCML20201World). Refer to the Agent documentation for more details about general NegMAS agents. Full information about the OneShotAgent and SCML2020Agent classes can be found here and here respectively.

As a participant in SCML, you only need to develop an Agent class inherited from the appropriate base class depending on the track.

So now we have the World and the Agent objects, and we already said that the agent does not directly interact with the world. How does these two agents interact then?

• When the World wants to interact with the Agent, it calls some method in it. For example, to instruct the agent to initialize itself, the world calls the init() method defined by the Agent. To inform the agent that a negotiation it is involved in is concluded with success, the World calls the method on_negotiation_success() defined by the agent.

• When the Agent wants to interact with the World, it accesses an interface object called an AgentWorldInterface or AWI for short which provides all the services available to the Agent. For example, to request a negotiation with another agent, the Agent object needs to call request_negotiation() defined in the AWI.

The world designer usually defines an AWI for its world that inherits NegMAS’s AgentWorldInterface class and provides any special services for agents interacting in this world. You can find all the services available to your agent through the AgentWorldInterface here. These methods and properties are still available for your agent in SCML. Nevertheless, in many cases, more convenient ways to access some of the information (e.g. the bulletin board) is provided in the specific AWIs implemented in the SCML package to be described now.

For SCML, there are – as you may already expect – two AWIs. The first is called OneShotAWI and is defined in scml.scml2020 to go with SCML2021World and SCML2020Agent and the second is defined in scml.oneshot to go with SCML2020OneShotWorld and OneShotAgent. An earlier version is also defined in scml.scml2019 which is a discontinued form of the game used in ANAC 2019 competition. You can find all the services provided by these AWIs here for the OneShot game (OneShot track) and here for the full game (Standard and Collusion tracks).

Now that we know how worlds and agents work and interact, we can look at how negotiation is managed in NegMAS.

A negotiation is controlled by a Mechanism object which implements the negotiation protocol (e.g. the alternating offers protocol). NegMAS provides several mediated and unmediated negotiation protocols (as well as auction mechanisms). The specific Mechanism that is used in SCML is the SAOMechanism which implements the bargaining protocol.

Negotiation strategies are implemented in a Negotiator object which usually inherits some base negotiator-class corresponding to the mechanism(s) it supports. The specific base class of all negotiators in SCML is SAONegotiator.

The interaction between Mechanism and Negotiator objects mirrors the interaction between World and Agent objects. Mechanism objects call methods in Negotiator objects directly but Negotiator objects can only access services provided by the Mechanism object through a AgentMechanismInterface (AMI). Note that it is an AMI not a NMI (for historical reasons). You can find more details about the general NegMAS AMI here.

Each specific Mechanism defines a corresponding specific AgentMechanismInterface class (in the same way that World classes define their own AWI). The SAOMechanism used in SCML defines SAOAMI for this purpose (Details are here).

To negotiate effectively, negotiators employ a UtilityFunction to represent their preferences over different possible Outcomes of the negotiation (where an outcome is a full assignment of values to all negotiated Issues). NegMAS provides an extensive set of utility function, and issue types. Please refer to NegMAS overview and tutorials for more details. NegMAS also provides some basic SAONegotiators for the SAOMechanism (Check the class diagram here). Moreover, you can access almost all Genius using NegMAS’s GeniusNegotiator including all finalists and winners of all past ANAC competitions.

Now we understand how agents interact with worlds through AWIs and negotiators interact with mechanisms through AMIs. We know that the general simulation is controlled by the world while each negotiation is controlled by a mechanism within that world. We need now to connect these two triplets of objects

As the figure above shows: Negotiator objects can be created and controlled by Agent objects for the purpose of negotiating with other Agent objects. The standard flow of operations is something like this:

1. Agent A uses its AWI to request_negotiation() with Agent B passing a Negotiator to be used in this negotiation. Usually Agent A will also create a UtilityFunction and attach it to the Negotiator it just created (by setting its ufun attribute).

2. The World calls Agent B’s respond_to_negotiation_request() asking it to provide its own Negotiator to negotiate with Agent A’s Negotiator. It can also just reject the negotiation request by returning no negotiators.

3. The World will then create a Mechanism and ask both Negotiators to join it. If all goes well, the negotiation starts (at a time defined by the simulation rules) and runs until either an agreement or disagreement is reached.

4. The World class will then inform Agents A and B about the results of the negotiation using their on_negotiation_success and on_negotiation_failure callbacks.

5. Successful negotiations lead to Agreements but are still not binding in general until signed by all agents involved (A and B in this case). Agent’s ’sign_all_contracts is used for this.

6. Signed agreements become Contracts and are executed (as specified in the simulation rules) by the World.

The full SCML game uses this complete flow. The OneShot game simplifies things by automatically requesting all negotiations, accepting all requests and signing all contracts.

When negotiations are independent, these are all the objects needed. Nevertheless, in many cases, including the SCM case, negotiations are inter-dependent. This means that what is good in one negotiation depends on other concurrently running negotiations (or on expectations of future negotiations). NegMAS provides two ways to support this case shown in the following figure:

controllers

1. Let Negotiators use UtilityFunctions that depend on some common state. That is what is happening in the left two negotiations. Because you define your own utility functions in the full SCML game (standard and collusion tracks), this is one possible way to orchestrate the behavior of your negotiators.

2. Have multiple Negotiators be controlled by a single Controller object with its own utility function that depends on what is happening on all the negotiations controlled. This is what is happening in the two negotiations on the right. This is also possible in the full SCML game (standard and collusion tracks) and is the only method available in the OneShot game (OneShot track).

The Negotiators connected to a controller lost their autonomy and just pass control to their owning Controller.

This concludes our introduction to NegMAS and different objects you need to know about to develop your agent.

In summary, you will need to develop an SCML2020Agent or OneShotAgent depending on the track. This agent uses an AWI or OneShotAWI to access sense and act in the world. All negotiations are controlled by SAONegotiators that may optionally be grouped and controlled centrally by Controller objects. The preferences of these negotiators and controllers are defined using UtilityFunction objects.

# A brief introduction to SCM¶

Here we only provide a 10,000-feet birds-eye view of the two games used in SCML 2021’s three tracks. Each section provides pointers to more information.

## The Oneshot game (OneShot)¶

An overview of the SCML-OneShot game is available here and a full description for the details-savy person is available here.

oneshot

The game runs in a world in which factories can run manufacturing processes to convert products into other products. There are exactly 3 products and 2 processes as shown in the figure. Each agent controls exactly one factory.

The agents that receive the raw material and generate the intermediate product are called 𝐿0 agents (level zero) and the agents. receiving the intermediate product and generate the final product are called 𝐿1 factories (level 1). The product type consumed by the factory controlled by an agent is called its input product and the product it produces is called its output product.

Every day (simulation step), each 𝐿0 factory receives one exogenous contract specifying a quantity and a unit price (supplies) for this day and each 𝐿1 factory receives one exogenous contract specifying a quantity and a unit price of the final product (sales) for the same day. Production and transporation are assumed to take no time.

𝐿0 and 𝐿1 agents need to negotiate together to secure intermediate product contracts in order to use their supplies (for 𝐿0 agents) or satisfy their sale obligations (for 𝐿1 agents). All products that remain unsold at the end of the day perish (i.e. has no value). Moreover, agents pay a disposal cost for any input products they buy and never sell and pay a shortfall penalty for any sales they cannot satisfy.

Your goal as an agent designer is to maximize your overall profit over the simulation time.

## The Full game (Standard/Collusion Tracks)¶

An overview of the SCML-Standard/Collusion game is available here and a full description for the details-savy person is available here.

oneshot

The game runs in a world similar to the one-shot game world but with few differences (that make a lot of difference):

• The production graph is depth is not limited to 2 processes. This means that some agents (in the middle) will have NO exogenous contracts and their goal will still be to match inputs and outputs but without being forced to either a specific supply or sales contract.

• Each agent (in the first and last production layers) can have multiple exogenous agreements.

• Agreements are not binding until signed into contracts. Each agent will have a chance to either sign or not sign (cancel) any agreement at the end of the simulated day (including exogenous agreements). This has two major implications:

1. The good news is that the agent can choose some input contracts and some output contracts to maximize its profit after all agreements are conducting ensuring that there are no other agreements (on that day) that may affect expected utility.

2. The bad news is that other agents have the same capability which means that an agent cannot trust that an agreeement it got and even signed will end up becoming binding as a contract.

• Production takes one step which means that you can sell today products that you bought yesterday or earlier but not today.

• Products do not perish and are valued at some fraction of their trading prices at the end of the simulation. This means that it makes sense in some cases for agents to pile stock (storage size is infinite and there is no storage or disposal cost). For example, if for some reason an agent believes that its input product will become more expesive over time, it makes sense for it to buy and accomulate input products early and then sell them when the price of the output product is at its max.

• Delivery time becomes an important negotiation issue. In the OneShot case seen earlier, buying, producing, selling and perishing happen in the same day (simulation step) which means that delivery time is obviously the same day. In this full game case, delivery time will usually be sometime in the future and it can theoretically be any day in the future up to the last simulated day.

• Agents decide their negotiation agendas and partners. This means that the system does not limit the price ranges for negotiation neither does it limit the quantity range or the delivery time range. This means that agents have to decide good ranges for themselves and have to be careful that the negotiation agenda offered by other agents may be so biased against them. In the OneShot game, none of this is possible because the system decides the negotiation agenda and they are always around the trding prices and with maximum quantities not exceeded the production capacity of agents.

• There is no disposal cost (products are never disposed of) or storage cost. Moreover, there is no pre-defined shortfall penalty. Instead the system simulates a spot market and forces agents with shortfalls to buy the remaining items they could not produce from that spot market. This mechanism is similar to predefined shortfall penalties with one major difference. The more an agent buys from the spot market, the more it needs to pay (per unit) in the future to buy from it again. This introduces a timing effect on failure to honor sell contracts through production. In the OneShot game, failing to honor a sell contract in the first step of the simulation and in the last step do not have much difference (assuming trading prices stayed the same). In this full game, failure to honor sell contracts early in the simulation will lead to higher spot market prices for that agent which will hurt it later in the game if it ever needed to buy from the spot market again.

• Finally, the fact that agents can have contracts with delivery dates in the future, complicates bankruptcy processing. When an agent goes bankrupt (i.e. its balance becomes negative), it is liquidated by selling everything in its inventory in the spot market and using the proceeds to pay compensation for agents with future contracts with it. None of this is needed in the OneShot game because agents do not have future contracts and going bankrupt does not prevent partners with contracts at the same step from getting the products they agreed upon at the agreed upon price.

• The only difference between the standard and collusion tracks in SCML 2021 is that in the former, there is a single instantiation of each competitor’s agent class (type) in the world while there are multiple instances of it in the later. This means that agents of the same class (that can find themselves easily by sharing class level attributes for example) can try to collude together to corner the market and increase their aggregated profits above what each of them could have achieved on its own in the collusion track.

# Run a session of the SCML world (2021)¶

The SCML world (Supply Chain Management League) runs on top of NegMAS.

In this tutorial, you will test a run of this world.

Firstly, let’s import everything from the SCML app

Note that the 2021 standard and collusion leagues are almost the same as 2020 versions with minor changes in the configuration and available data to the agent which will be explained later. All agents developed for SCML2020 run with no modification in SCML2021’s standard and collusion leagues. Moreover, agents devleoped for SCML-OneShot run normally in SCML2021 worlds.

from scml.scml2020 import *
from scml.oneshot import *


There are several ways to create an SCML world. One of the simplest is to use the generate method of the SCML2021World class. This will create a dict that can be passed to the SCML2021World constructor as keyword arguments.

Here we explicitly set construct_graphs to True which slows the simulation yet allows us to see graph representations of what is happening in the world.

agent_types = [DecentralizingAgent,
MarketAwareDecentralizingAgent,
SyncRandomOneShotAgent]


Notice that we have a one-shot agent in the mix. Even though one-shot agents are designed for SCML2020OneShot worlds, they can run with no modification in SCML2020 and SCML2021 worlds.

world = SCML2021World(
**SCML2021World.generate(
agent_types=agent_types,
n_steps=50
),
construct_graphs=True,
)


Let’s draw a graph to see what is in this world

_, _ = world.draw()


If you want to just test your installation (and do not care whether you get an accurate indicator of agent performance), you can set the number of steps to a small value (e.g. n_steps=10).

Now you can run this world simulation by just calling run.

world.run_with_progress() # may take few minutes


Let’s see what happened in this run. Firstly, how many negotiations were conducted over time

plt.plot(world.stats['n_negotiations'])
plt.xlabel('Simulation Step')
plt.ylabel('N. Negotiations')
plt.show()


It is clear that many negotiations happened at the beginning of the simulation with smaller number later. That is expected as the agents at the first and last production layer receive more exogenous contracts in the beginning.

Several other market statistics are available:

pprint(list(_ for _ in world.stats.keys() if "@" not in _  ))

['n_contracts_nullified_now',
'n_bankrupt',
'sold_quantity_0',
'unit_price_0',
'sold_quantity_1',
'unit_price_1',
'sold_quantity_2',
'unit_price_2',
'productivity',
'market_size',
'production_failures',
'bankruptcy',
'n_registered_negotiations_before',
'n_contracts_executed',
'n_contracts_erred',
'n_contracts_nullified',
'n_contracts_cancelled',
'n_contracts_dropped',
'n_breaches',
'breach_level',
'n_contracts_signed',
'n_contracts_concluded',
'n_negotiations',
'n_negotiation_rounds_successful',
'n_negotiation_rounds_failed',
'n_negotiation_successful',
'n_negotiation_failed',
'n_registered_negotiations_after',
'activity_level',
'step_time',
'total_time']


Let’s start by seeing how long did each step take (note that stats access the stats as a Dict[str, List] but stats_df access the same data as a pandas dataframe.

plt.bar(range(world.n_steps), world.stats_df['step_time'])
plt.xlabel('Simulation Step')
plt.ylabel('Time (s)')
plt.show()


There are statistics specific for each agent that all have “_{agent_name}”. Lets check what is available for the winner agent:

winner = world.winners[0]
pprint(list(_ for _ in world.stats.keys() if winner.name in _ ))

['spot_market_quantity_02MAD@1',


The convension is that agent names has the form {ind}{Type}{process} where ind is a unique index, Type is a shortened version of the agent’s type name, and process is the process the agnet can run. Note that the agent’s input product has the same number as its process and its output product has the next number (i.e. an agent that runs process 1, has input product 1 and output product 2).

We can see that 8 pieces of information are available (for each time-step of the simulation):

• bankrupt If true, the agent is bankrupt.

• balance The money the agent has in its wallet (account).

• inventory (input) The number of units of the agent’s input product available in its inventory (by the end of the simulation step).

• inventory (output) The number of units of the agent’s output product available in its inventory (by the end of the simulation step).

• assets The value of the agent’s assets (input and output products in inventory) evaluated at the trading price

• spot market quantity The quantity bought by this agent from the spot market (of its output product on this step). This can only happen as a result of a product-breach.

• spot market loss The spot market price for the agent. This value will go up the more the agent buys from the spot market and will be used to calculate the price for this agent at future steps. This way agents that depend on the spot market instead of negotiation get punished.

• productivity The fraction of the agent’s production lines that were active at a given time-step.

• score The score of the agent according to the evaluation rule of ANAC SCML 2020

Let’s see how did our agent do

#show the first and last value of each of the agent statistics
pprint({k:(v[0], v[-1]) for k, v in world.stats.items() if winner.name in k })

{'assets_02MAD@1': (0.0, 9444.614261016386),

stats = pd.DataFrame(data=world.stats)
fig, axs = plt.subplots(2, 3)
for ax, key in zip(axs.flatten().tolist(), ["score", "balance", "assets", "productivity",
"spot_market_quantity", "spot_market_loss"]):
ax.plot(stats[f"{key}_{winner}"])
ax.set(ylabel=key)
fig.show()


We can for example check the activity level of this world (defined as the total amount of money transferred due to trade)

plt.plot(world.stats['activity_level'])
plt.xlabel('Simulation Step')
plt.ylabel('Activitiy Level ($)\nTotal Money Transferred') plt.show()  We can see a picture of contracting in this world as follows: plt.plot(world.stats['n_contracts_concluded'], label='Concluded Contracts') plt.plot(world.stats['n_contracts_cancelled'], label='Cancelled Contracts') plt.plot(world.stats['n_contracts_signed'], label='Signed Contracts') plt.plot(world.stats['n_contracts_executed'], label='Executed Contracts') plt.legend() plt.xlabel('Simulation Step') plt.ylabel('N. Contracts') plt.show()  We can also check the breaches that happened plt.plot(world.stats['breach_level']) plt.xlabel('Simulation Step') plt.ylabel('Total Breach Level') plt.show()  Notice that there can be multiple winners winner_profits = [100 * world.scores()[_.id] for _ in world.winners] winner_types = [_.short_type_name for _ in world.winners] print(f"{world.winners} of type {winner_types} won at {winner_profits}%")  [02MAD@1] of type ['marketawaredecentralizing'] won at [97.12723761553673]%  Let’s check how did the first winner’s inventory changes over time: # find the keys in stats for the input and output inventory in_key = [_ for _ in world.stats.keys() if _.startswith(f'inventory_{winner}_input')][0] out_key = [_ for _ in world.stats.keys() if _.startswith(f'inventory_{winner}_output')][0] # find input and output product indices input_product, output_product = winner.awi.my_input_product, winner.awi.my_output_product # draw fig, (quantity, value) = plt.subplots(1, 2) quantity.plot(world.stats[in_key], label=f"Input Product") quantity.plot(world.stats[out_key], label=f"Output Product") quantity.set(xlabel='Simulation Step', ylabel='Winner\'s Total Storage (item)') quantity.legend() value.plot(np.array(world.stats[in_key]) * np.array(world.stats[f"trading_price_{input_product}"]) , label=f"Input Product") value.plot(np.array(world.stats[out_key]) * np.array(world.stats[f"trading_price_{output_product}"]) , label=f"Output Product") value.set(xlabel='Simulation Step', ylabel='Winner\'s Inventory Value ($)')
value.legend()
fig.show()


We can actually check what happens to ALL competitors:

from scml.scml2020.world import is_system_agent
fig, (profit, score) = plt.subplots(1, 2)
snames = sorted(world.non_system_agent_names)
for name in snames:
profit.plot(100.0 * (np.asarray(world.stats[f'balance_{name}'])/world.stats[f'balance_{name}'][0] - 1.0), label=name)
score.plot(100 * np.asarray(world.stats[f'score_{name}']), label=name)
profit.set(xlabel='Simulation Step', ylabel='Player Profit Ignoring Inventory (%)')
profit.legend(loc='lower left')
score.set(xlabel='Simulation Step', ylabel='Player Score (%)')
fig.show()

from scml.scml2020.world import is_system_agent
fig, (profit, score) = plt.subplots(1, 2)
snames = sorted(world.non_system_agent_names)
for name in snames:
profit.plot((np.asarray(world.stats[f'balance_{name}'])), label=name)
score.plot(np.asarray(world.stats[f'score_{name}'])*(world.stats[f'balance_{name}'][0]), label=name)
profit.set(xlabel='Simulation Step', ylabel='Player Balance ($)') profit.legend(loc='lower left') score.set(xlabel='Simulation Step', ylabel='Player Score Unnormalized ($)')
fig.show()


or just look at the end of the game

fig, (score, profit) = plt.subplots(1, 2)
final_scores = [100 * world.stats[f"score_{_}"][-1]
for _ in world.non_system_agent_names]
final_profits = [100 * world.stats[f"balance_{_}"][-1] / world.stats[f"balance_{_}"][0] - 100
for _ in world.non_system_agent_names]
plt.setp(score.xaxis.get_majorticklabels(), rotation=45)
plt.setp(profit.xaxis.get_majorticklabels(), rotation=45)
score.bar(world.non_system_agent_names, final_scores)
profit.bar(world.non_system_agent_names, final_profits)
score.set(ylabel="Final Score (%)")
profit.set(ylabel="Final Profit (%)")

fig.show()

fig, (score, profit) = plt.subplots(1, 2)
final_scores = [world.stats[f"score_{_}"][-1] * (world.stats[f"balance_{_}"][0])
for _ in world.non_system_agent_names]
final_profits = [world.stats[f"balance_{_}"][-1]
for _ in world.non_system_agent_names]
plt.setp(score.xaxis.get_majorticklabels(), rotation=45)
plt.setp(profit.xaxis.get_majorticklabels(), rotation=45)
score.bar(world.non_system_agent_names, final_scores)
profit.bar(world.non_system_agent_names, final_profits)
score.set(ylabel="Final Unnormalized Score ($)") profit.set(ylabel="Final Balance ($)")

fig.show()


You can inspect what happened in the simulation by plotting different output statistics. For example, we can see how did the trading price of different products change over the simulation time.

fig, axs = plt.subplots(2, 2)
for ax, key in zip(axs.flatten().tolist(), ["trading_price", "sold_quantity", "unit_price"]):
for p in range(world.n_products):
ax.plot(world.stats[f"{key}_{p}"], marker="x", label=f"Product {p}")
ax.set_ylabel(key.replace("_", " ").title())
ax.legend().set_visible(False)
axs[-1, 0].legend(bbox_to_anchor=(1, -.5), ncol=3)
fig.show()

fig, axs = plt.subplots(1, 2)
for ax, key in zip(axs.flatten().tolist(), ["spot_market_quantity", "spot_market_loss"]):
for a in world.non_system_agent_names:
ax.plot(world.stats[f"{key}_{a}"], marker="x", label=f"{a}")
ax.set_ylabel(key.replace("_", " ").title())
ax.legend().set_visible(False)
axs[0].legend(bbox_to_anchor=(1, -.2), ncol=4)
fig.show()


You can dig futher to understand what happened during this siumulation. For example, let’s see some of the contracts that were signed:

# create a view with only signed contracts
contracts = world.contracts_df
signed = contracts.loc[contracts.signed_at>=0, :]

fields = ["seller_name", "buyer_name", "delivery_time", "quantity", "unit_price",
"signed_at", "executed", "breached", "nullified", "erred"]

seller_name buyer_name delivery_time quantity unit_price signed_at executed breached nullified erred
542 01SRO@0 02MAD@1 24 54 16 21 True False False False
563 01SRO@0 02MAD@1 24 51 11 23 True False False False
603 01SRO@0 02MAD@1 31 49 16 26 True False False False
438 01SRO@0 02MAD@1 16 49 13 14 True False False False
649 01SRO@0 02MAD@1 33 43 11 31 True False False False
361 01SRO@0 02MAD@1 11 42 13 8 True False False False
486 01SRO@0 02MAD@1 17 38 20 17 True False False False
626 01SRO@0 02MAD@1 32 35 11 27 True False False False
634 01SRO@0 02MAD@1 36 27 11 28 True False False False
530 01SRO@0 02MAD@1 21 24 11 21 True False False False

Let’s check some of the contracts that were fully executed

signed.loc[signed.executed, fields].sort_values(["quantity", "unit_price"], ascending=False).head(10)

seller_name buyer_name delivery_time quantity unit_price signed_at executed breached nullified erred
542 01SRO@0 02MAD@1 24 54 16 21 True False False False
563 01SRO@0 02MAD@1 24 51 11 23 True False False False
603 01SRO@0 02MAD@1 31 49 16 26 True False False False
438 01SRO@0 02MAD@1 16 49 13 14 True False False False
649 01SRO@0 02MAD@1 33 43 11 31 True False False False
361 01SRO@0 02MAD@1 11 42 13 8 True False False False
486 01SRO@0 02MAD@1 17 38 20 17 True False False False
626 01SRO@0 02MAD@1 32 35 11 27 True False False False
634 01SRO@0 02MAD@1 36 27 11 28 True False False False
530 01SRO@0 02MAD@1 21 24 11 21 True False False False
signed.loc[signed.breached, fields[:-4] + ["breaches"]].sort_values(["quantity", "unit_price"], ascending=False).head(10)

seller_name buyer_name delivery_time quantity unit_price signed_at breaches

We can now see how does the singning day affect delivery day, product and quantity

fig, ax = plt.subplots(1, 3)
for i, x in enumerate(["delivery_time", "quantity", "product_index"]):
ax[i].scatter(signed.signed_at, signed[x])
ax[i].set(ylabel=x.replace("_", " ").title(), xlabel="Signing Day")
fig.show()

fig, ax = plt.subplots(1, 3)
for i, x in enumerate(["delivery_time", "unit_price", "product_index"]):
ax[i].scatter(signed.quantity, signed[x])
ax[i].set(ylabel=x.replace("_", " ").title(), xlabel="Quantity")
fig.show()


Did any agents go bankrupt and when?

bankruptcy = {a: np.nonzero(stats[f"bankrupt_{a}"].values)[0]
for a in world.non_system_agent_names}
pprint({k: "No" if len(v)<1 else f"at: {v[0]}" for k, v in bankruptcy.items()})

{'00MAD@0': 'No', '01SRO@0': 'No', '02MAD@1': 'No', '03SRO@1': 'No'}


You can see what happened during this simulation by drawing graphs at different steps. The meaning of different edge colors can be drawn as follows:

from negmas import show_edge_colors
show_edge_colors()


You can see what happened in this world in a series of graphs using the draw method

world.draw(steps=(0, world.n_steps), together=False, ncols=2, figsize=(20, 20))
plt.show()


You can also run a simple animation to see what happens at every step (you need to download the jupyter notebook and execute it to see the animation) :

world.save_gif("run.gif")

[]


then show the animation

from IPython.display import HTML
HTML('<img src="run.gif">')


# Running a tournament¶

Now that you can run simple world simulations, let’s try to run a complete tournament and see its results. Let’s start by running a standard tournament (in which each agent is represented by a single factory). Running a collusion tournament will be exactly the same with the only difference that anac2021_std will be replaced with anac2021_collusion.Running a one-shot tournament (new in 2021) similarily just requires a change from anac2021_std to anac2021_oneshot

Note that in the real competition we use thousands of configurations and longer simulation steps (e.g. 50 $$\le$$ n_steps $$\le$$ 500).

from scml.scml2020.utils import anac2021_std
pd.options.display.float_format = '{:,.2f}'.format
def shorten_names(results):
# just make agent types more readable
results.score_stats.agent_type = results.score_stats.agent_type.str.split(".").str[-1]
results.kstest.a = results.kstest.a.str.split(".").str[-1]
results.kstest.b = results.kstest.b.str.split(".").str[-1]
results.total_scores.agent_type = results.total_scores.agent_type.str.split(".").str[-1]
results.scores.agent_type = results.scores.agent_type.str.split(".").str[-1]
results.winners = [_.split(".")[-1] for _ in results.winners]
return results

tournament_types = [
DecentralizingAgent, MarketAwareDecentralizingAgent, SyncRandomOneShotAgent
]
# may take a long time
results = anac2021_std(
competitors=tournament_types,
n_configs=5, # number of different configurations to generate
n_runs_per_world=1, # number of times to repeat every simulation (with agent assignment)
n_steps = 10, # number of days (simulation steps) per simulation
print_exceptions=True,
)

results = shorten_names(results)


Who was the winner?

results.winners

['DecentralizingAgent']


How many simulations were actually run?

len(results.scores.run_id.unique())

15


The total number of simulations $$n_{s}$$ will be $$n_t \times n_c \times n_r$$ where $$n_t$$ is the number of competitor agent types, $$n_c$$ is the number of configurations, and $$n_r$$ is the number of runs per configuration

We can also see the scores that every agent type got

results.score_stats

agent_type count mean std min 25% 50% 75% max
0 SyncRandomOneShotAgent 15.00 -0.30 0.17 -0.62 -0.35 -0.28 -0.16 -0.10
1 DecentralizingAgent 15.00 -0.12 0.13 -0.39 -0.22 -0.06 -0.04 0.05
2 MarketAwareDecentralizingAgent 15.00 -0.13 0.12 -0.39 -0.22 -0.07 -0.04 0.00

You can also do statistical significance testing using ttest or kstest (with multi-comparison correction)

results.kstest

a b t p n_a n_b n_effective
0 SyncRandomOneShotAgent DecentralizingAgent 0.53 0.03 15 15 15
1 SyncRandomOneShotAgent MarketAwareDecentralizingAgent 0.53 0.03 15 15 15
2 DecentralizingAgent MarketAwareDecentralizingAgent 0.13 1.00 15 15 15

see the total score

results.total_scores

agent_type score
0 DecentralizingAgent -0.12
1 MarketAwareDecentralizingAgent -0.13
2 SyncRandomOneShotAgent -0.30

or the aggregated statistics of the world. For example, let’s draw the activity level for different simulations.

plt.errorbar(range(len(results.agg_stats)),
results.agg_stats.activity_level_mean,
np.sqrt(results.agg_stats.activity_level_var)
)
plt.xlabel("Simulation Number")
plt.ylabel("Activity Level")
plt.show()


We can even get the scores of every agent belonging to every agent type at every simulation

results.scores.loc[:, ["agent_name", "agent_type", "score"]].head()

agent_name agent_type score
0 00SRO@0 SyncRandomOneShotAgent -0.11
1 01Dec@0 DecentralizingAgent -0.22
3 00SRO@0 SyncRandomOneShotAgent -0.34

Let’s see how did the location at the production graph affect the score of each type.

results.scores["level"] = results.scores.agent_name.str.split("@", expand=True).loc[:, 1]
results.scores = results.scores.sort_values("level")
sns.lineplot(data=results.scores[["agent_type", "level", "score"]],
x="level", y="score", hue="agent_type", ci=None)
plt.plot([0.0] * len(results.scores["level"].unique()), "b--")
plt.show()


# Running a One-Shot tournament¶

In 2021, a new track was introduced to the SCM league called the SCML-OneShot track which simplified the problem in order to focus research efforts on the core many-to-many concurrent negotiation challeng. You can run a tournament for this league in almost the same way as we did with standard/collusion tournaments using anac2021_oneshot

Note that in the real competition we use thousands of configurations and longer simulation steps (e.g. 50 $$\le$$ n_steps $$\le$$ 500).

from scml.scml2020.utils import anac2021_oneshot

tournament_types = [RandomOneShotAgent, SyncRandomOneShotAgent, GreedyOneShotAgent, GreedySingleAgreementAgent]
# may take a long time
results = anac2021_oneshot(
competitors=tournament_types,
n_configs=5, # number of different configurations to generate
n_runs_per_world=1, # number of times to repeat every simulation (with agent assignment)
n_steps = 10, # number of days (simulation steps) per simulation
print_exceptions=True,
)
results = shorten_names(results)


Who was the winner?

results.winners

['GreedyOneShotAgent']


How many simulations were actually run?

len(results.scores.run_id.unique())

20


The total number of simulations $$n_{s}$$ will be $$n_t \times n_c \times n_r$$ where $$n_t$$ is the number of competitor agent types, $$n_c$$ is the number of configurations, and $$n_r$$ is the number of runs per configuration

We can also see the scores that every agent type got

results.score_stats

agent_type count mean std min 25% 50% 75% max
0 GreedyOneShotAgent 20.00 1.10 0.16 0.89 0.98 1.09 1.17 1.43
1 GreedySingleAgreementAgent 20.00 0.80 0.09 0.62 0.75 0.79 0.86 0.96
2 RandomOneShotAgent 20.00 0.61 0.26 0.16 0.36 0.62 0.83 0.99
3 SyncRandomOneShotAgent 20.00 0.63 0.25 0.14 0.55 0.64 0.80 1.12

You can also do statistical significance testing using ttest or kstest (with multi-comparison correction)

results.kstest

a b t p n_a n_b n_effective
0 RandomOneShotAgent GreedyOneShotAgent 0.85 0.00 20 20 20
1 RandomOneShotAgent GreedySingleAgreementAgent 0.60 0.00 20 20 20
2 RandomOneShotAgent SyncRandomOneShotAgent 0.30 0.34 20 20 20
3 GreedyOneShotAgent GreedySingleAgreementAgent 0.80 0.00 20 20 20
4 GreedyOneShotAgent SyncRandomOneShotAgent 0.95 0.00 20 20 20
5 GreedySingleAgreementAgent SyncRandomOneShotAgent 0.55 0.00 20 20 20

see the total score

results.total_scores

agent_type score
0 GreedyOneShotAgent 1.10
1 GreedySingleAgreementAgent 0.80
2 SyncRandomOneShotAgent 0.63
3 RandomOneShotAgent 0.61

We can even get the scores of every agent belonging to every agent type at every simulation

results.scores.loc[:, ["agent_name", "agent_type", "score"]].head()

agent_name agent_type score
0 00Ran@0 RandomOneShotAgent 0.86
1 01Gre@0 GreedyOneShotAgent 1.11
2 06GSA@1 GreedySingleAgreementAgent 0.79
3 07SyR@1 SyncRandomOneShotAgent 0.14
4 00Gre@0 GreedyOneShotAgent 1.43

Let’s see how did the location at the production graph affect the score of each type.

results.scores["level"] = results.scores.agent_name.str.split("@", expand=True).loc[:, 1]
results.scores = results.scores.sort_values("level")
sns.lineplot(data=results.scores[["agent_type", "level", "score"]],
x="level", y="score", hue="agent_type")
plt.plot([0.0] * len(results.scores["level"].unique()), "b--")
plt.show()


Now that you can run simulations and complete tournament, let’s see how are we going to develop a new agent for the SCML2021 league $$\rightarrow$$

Download Notebook.