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A Pre-Survey
After an internal pilot with 9 participants, the entire CaSiNo dataset was collected on Amazon Mechanical Turk over a period of a month. In total, 846 subjects took part in our data collection study. The statistics presented in this section are based on self-identified demographical attributes and standard ways of collecting personality traits from the literature. We had a highly diverse participant pool, representing different age groups, gender, ethnic backgrounds and education levels. The mean Age among our participants is 36.97 with a standard deviation of 10.81. One participant was removed from this computation since the age entered was 3, which we believed to be in error. Among the participants, 472 identified themselves as Female, 372 were Male, and 2 belonged to Other category. While most of the participants were White American (625 in count), our study also involved a mix of Asian American, Black or African American, Hispanic or Latino, and Multi-Racial groups, among others. Most common highest level of education was found to be a 4-year Bachelor degree (346 participants), although the complete pool represents a mixture of Master and PhD degree holders, 2-year and 4-year college graduates without degrees, and high school graduates, among others.
For the personality traits, 364 participants were classified as Proself, 463 as Prosocial, and 19 were unclassified based on their Social Value Orientation<sup>2</sup>. The mean scores for the Big-5 personality traits were found to be as follows: Agreeableness: 5.27, Conscientiousness: 5.6, Emotional Stability: 4.91, Extraversion: 3.69, Openness to Experiences: 5.04. We use the Ten-Item Personality Inventory (TIPI)<sup>3</sup> to compute these attributes, where each of them takes a value between 1 and 7.
B Preparation Phase
We present the scenario description seen by the participants in Table 4. Several arguments that the participants come up with are presented in Table 5.
Imagine that you are on a camping trip! Woohoo! Apart from some basic amount of supplies which are provided to everyone, you can collect some additional food packages, water bottles and firewood, to make your camping trip even better. Since these are limited in quantity, you will have to split these additional packages with your campsite neighbor! Each of these items will be of either High, Medium or Low priority for you. Each of them only has an available quantity of 3. You will negotiate with another MTurker by chatting in English, using reasons from your personal experiences to justify why you need additional packages apart from the basic supplies. Try hard to get as many items as you can!
Table 4: The camping scenario description as seen by the participants in our data collection.
C Data Post-processing steps
We list the data post-processing and filtering steps below:
1. Removal of incomplete dialogues: During the data collection, many negotiation sessions could not be completed due to one of the participants' disconnecting in the middle. Any dialogue for which we had missing data, including pre-survey and post-survey responses for both the participants, was removed from the final dataset.
2. Removal of bad quality dialogues: We also removed dialogues where we observed a lack of effort or an irrelevant dialogue between the participants. We removed dialogues where the participants used very short utterances or failed to answer the dummy questions about their own preferences correctly, suggesting a lack of effort. Further, we removed the instances where the participants talked about the MTurk task itself, rather than the negotiation. These cases were identified based on a list of keywords: {'mturk', 'amt', 'turns', 'messages', 'amazon', '10'}. In a few cases, it was possible to retain the complete dialogue structure by just removing a few utterances. Hence, in these cases, we only removed the irrelevant utterances, while retaining the rest of the dialogue and the associated metadata.
3. Tackling inappropriate language use: Rarely, some participants also used inappropriate language in their utterances. These dialogues were identified using the lexicon
$^{2}$ https://static1.squarespace.com/ static/523f28fce4b0f99c83f055f2/t/ 56c794cdf8baf3ae17cf188c/1455920333224/ Triple+Dominance+Measure+of+SVO.pdf  ${ }^{3}$ https://gosling.psy.utexas.edu/scales-weve-developed/ ten-item-personality-measure-tipi/ ten-item-personality-inventory-tipi/
Category	Item type
Food	Water	Firewood
Personal Care	because I'm normally eat more because of my big size	I have to take a lot of medicine so hydration is very important	I have arthritis and being sure I am warm is important for my comfort.
Recreational	Need many snacks throughout the day for energy to hike	I am a very active camper. I like to hike when I camp and I once ran out of water during a strenuous hike.	I like having campfires so I need all the firewood.
Group Needs	I have two teenage boys who require a lot of food, especially when expending so much energy with all the activities of camping.	I need more water because I have more people to keep hydrated and do not have enough.	I need more firewood due to having several people join on the trip and needing a bigger fire overall.
Emergency	Some could have been damaged during the trip. I would need more.	our car overheated we had to use the water	It may get cold and firewood can be hard to come by at certain campsites.
Table 5: Example arguments that the participants come up for their individual requirements during the preparation phase. The categories defined are not exhaustive.
of English swear words on Wikipedia<sup>4</sup>. All these dialogues were also removed from the final dataset.
D Participant Feedback
Role-playing has been a key technique to teach negotiation skills in classroom settings. One of the key application areas for automated negotiation systems is to augment such exercises by allowing the human participants to negotiate with an AI and practice their social skills. To maximize the utility of the system developed using our dataset, we choose the camping scenario, which we expected to be easily relatable for our participants and also for any individual who negotiates with a system developed on our dataset. This is essential to ensure that the collected dialogues are engaging, interesting, and capture the rich personal context of the individuals, albeit in a closed-domain setting. One way to judge whether the participants are able to relate to the scenario is via their feedback after the study. With this in mind, we used a feedback column in the Post-survey and asked several questions to the participants throughout the data collection process. These questions included: 1) How was your overall experience? 2) Were you able to see yourself in the 'role' and follow best practices?, 3) Could you relate to camping?, and 4) How helpful was the preparation phase?
Based on manual inspection, we observed an overall positive feedback for all the above questions. Most of the participants were able to easily
relate to camping. They frequently pointed out that the experience was 'fun', 'interesting', and 'nice'. Many saw this as an opportunity to talk to someone during these tough times of the pandemic. Several cherry-picked feedback responses which indicate that the participants enjoyed the task as a whole and were in fact able to connect well and engage in the negotiation, have been provided in Table 6.
E Correlational Analysis
The analysis discussed in the paper is presented in Tables 7, 8, 9, and 10.
F Strategy Prediction
F.1 Architecture
We provide some more details on the strategy prediction multi-task architecture in this section. The self-attention layer is itself represented using the BERT encoder architecture, but with a single transformer layer and just one attention head. After the self-attention layer, we first extract the 768 dimensional representation for the [CLS] token. This is passed through a feed-forward network, which converts it to 128 dimensions. The feature embedding is also converted to a 128 dimensional vector using a feed-forward network. Both the above embeddings are then combined using an element-wise summation, which further passes through two feedforward layers with hidden dimensions of 64 and 1, and a sigmoid layer to finally output the probability for each annotation strategy.
$^{4}$ https://en.wiktionary.org/wiki/ Category:English_swear_words
I could do this all day
I am camping right now!
My partner had better reasons for needing the firewood
I enjoyed talking about camping, I haven’t been in a while. It reminded me of all of the things that I used to do.
The best thing I did was ask him what his preferences were. He had no interest in firewood which was my highest priority.
Table 6: A few positive feedback responses which we obtained from the participants during the collection of the CaSiNo dataset.
Points-Scored	Satisfaction	Opp-Likeness
Points-Scored	1	.376**	.276**
Satisfaction	.376**	1	.702**
Opp-Likeness	.276**	.702**	1
PPoints-Scored	-.092**	.105**	.132**
PSatisfaction	.105**	.180**	.244**
POpp-Likeness	.132**	.244**	.344**
Table 7: Pearson Correlation Coefficients (r) between the outcome variables. Variables with P. prefix denote the corresponding attributes of the negotiation partner of an individual. These correlations have been computed on the entire CaSiNo dataset. * denotes significance with  $p < {0.05}$  (2-tailed). ** denotes significance with  $p < {0.01}$  (2-tailed).
F.2 Computing Infrastructure
All experiments were performed on a single Nvidia Tesla V100 GPU. The training takes two hours to complete for a single model on all the cross-validation folds.
F.3 Training Details
To search for the best hyperparameters, we use a combination of randomized and manual search for the Full model. For each cross fold,  $5\%$  of the training data was kept aside for validation. The metric for choosing the best hyper-parameters is the mean F1 score for the positive class on the validation dataset. The mean is over all the labels and over 5 cross-validation folds.
We vary the learning rate in  $\{3e^{-5}, 4e^{-5}, 5e^{-5}\}$ , weight decay in  $\{0.0, 0.01, 0.001\}$  and dropout in  $\{0.0, 0.1, 0.2, 0.3\}$ . The rest of the hyperparameters were fixed based on the available computational and space resources. We report the best performing hyper-parameters in the main paper, which were used for all the experiments. We report the performance on the validation set corresponding to the chosen hyper-parameters and the number of trainable parameters in Table 11.
G Screenshot from the data collection interface
To provide more clarity on the data collection procedure, we provide several screenshots from our interface in Figures 3, 4, 5, and 6. We design the
pre-survey using the Qualtrics platform. The rest of the data collection is based on the ParlAI framework (Miller et al., 2017).
5https://www_qualtrics.com/core-xm/survey-software/
Joint Points
Integrative potential	.425***
Table 8: Pearson Correlation Coefficient (r) between integrative potential and the joint negotiation performance. *** denotes significance with  $p < {0.001}$  .
Joint Points	Points-Scored	Satisfaction	Opp-Likeness	PPoints-Scored	P.Satisfaction	P.Opp-Likeness
Prosocial Generic
Small-Talk	-.022	-.002	.086*	.115**	-.025	.068	.127**
Prosocial About Preferences
No-Need	-.003	-.066	.035	.023	.063	.083*	.089*
Elicit-Pref	.053	.055	.058	.015	.010	.022	.055
Proself Generic
UV-Part	-.037	.008	-.051	-.112**	-.054	-.131**	-.151**
Vouch-Fairness	-.140**	-.084*	-.159**	-.196**	-.090*	-.185**	-.180**
Proself About Preferences
Self-Need	-.003	.022	-.061	-.065	-.026	-.091*	-.086*
Other-Need	-.176**	-.045	-.101**	-.118**	-.174**	-.160**	-.113**
Table 9: Pearson Correlation Coefficients (r) for strategy annotation counts with the outcome variables. Variables with P. prefix denote the corresponding attributes of the negotiation partner of an individual. These correlations have been computed on the annotated subset of the CaSiNo dataset. * denotes significance with  $p < {0.05}$  (2-tailed). ** denotes significance with  $p < {0.01}$  (2-tailed).
P.Small-Talk	P.Self-Need	P.Other-Need	P.No-Need	P.Elicit-Pref	P.UV-Part	P.Vouch-Fair
Small-Talk	.769**	-.033	.021	.063	-.059	-.012	-.180**
Self-Need	-.033	.355**	.103**	.115**	-.007	.235**	-.088*
Other-Need	.021	.103**	.339**	.002	-.067	.159**	-.015
No-Need	.063	.115**	.002	.258**	.097**	.064	-.116**
Elicit-Pref	-.059	-.007	-.067	.097**	.168**	-.097**	-.102**
UV-Part	-.012	.235**	.159**	.064	-.097**	.268**	.064
Vouch-Fair	-.180**	-.088*	-.015	-.116**	-.102**	.064	.287**
Table 10: Pearson Correlation Coefficients (r) between strategy annotation counts. Variables with P. prefix denote the corresponding attributes of the negotiation partner of an individual. These correlations have been computed on the annotated subset of the CaSiNo dataset. * denotes significance with  $p < 0.05$  (2-tailed). ** denotes significance with  $p < 0.01$  (2-tailed).
Model	Overall Validation F1	Trainable Parameters
Majority	0.0	0
LR-BoW	49.6	2646.2 (27.2)
BERT-FT	69.9	109,590,529
Multi-task training
Freeze	62.3	221,361,031
No Attn	66.6	110,235,271
No Feats	77.6	330,840,583
Full	78.1	330,844,807
+OS	77.9	330,844,807
+IDPT	79.6	330,844,807
+IDPT+OS	79.6	330,844,807
Table 11: Training details for the strategy prediction task. The Overall F1 scores are for the positive class. For LR-BoW, the exact number of features varies slightly based on the CV split. Hence, we report Mean (Std) across the five splits.
Negotiate a deal with another user! Earn upto $5 instantly for one HIT.
You will first be given a quick tutorial on how to negotiate. Then, you will negotiate with another MTurker to arrive at an agreement. You will earn $0.30 base pay and $1.70 on completion (when none of the partners disconnects mid-way). Additionally, you can earn upto $3 in bonus for one HIT, depending on performance and quality. The task is about 20 minutes. Turkers with too many disconnects will be blocked for similar HITs in the future.
Figure 3: Screenshots from the data collection interface: Task Preview. This is a brief task description which the MTurkers see before signing up for our data collection task.
Figure 4: Screenshots from the data collection interface: Participant On-boarding.
(a) Onboarding Phase 1: The first step takes the participant participants to come up with arguments from their past expertise to Qualtrics which collects the demographics, introduces the riciencies, which justify their preferences. The preference order camping scenario and gives a tutorial on negotiation best prac- is randomly assigned by us. This provides a personal context tices. around the negotiation for each participant.
(a) Chat Interface: The right portion allows two participants to negotiate in English using alternating messages. They also have the option to use emoticons. Once they come to an agreement, one of the participant must enter the exact deal on the left.
Figure 5: Screenshots from the data collection interface: Chat Interface.
(b) Response to the Deal: When one of the participants enters the deal, the other gets an option to either accept, reject, or walk away from the deal. In the CaSiNO dataset, a participant walks away in 36 dialogues.